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āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļāļāļāļļāļ
AI āļāļāļāđāļĢāļēāļāļ°āļāđāļēāļāđāļŦāđ āđāļĨāđāļ§āļŦāļēāļāļēāļāļāļĩāđāđāļāđāļŠāļģāļŦāļĢāļąāļāļāļļāļ
āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļąāļāļĐāļ°:
Data Warehousing, Risk Management, Data Analysis, TensorFlow, Big Data
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, develop and deploy advanced machine learning and artificial intelligence models focused on financial risk analytics, credit risk assessment, market risk analysis and fraud detection.
- Lead the end-to-end data science lifecycle, from problem definition and data exploration through model validation, implementation and ongoing performance monitoring.
- Mentor and supervise junior data scientists and analytical staff, fostering a culture of excellence, continuous learning and collaborative problem-solving within the te ...
- Conduct comprehensive exploratory data analysis on large-scale financial datasets to identify patterns, anomalies and opportunities for predictive modelling.
- Develop robust statistical models and econometric frameworks to quantify and forecast financial risks across various asset classes and business segments.
- Create clear and compelling data visualisations and reports that translate complex analytical findings into actionable business recommendations for senior management and risk committees.
- Collaborate closely with risk management, compliance, trading and operations teams to understand business requirements and ensure analytical solutions align with strategic objectives.
- Establish and maintain rigorous model governance frameworks, including validation protocols, backtesting procedures and documentation standards to ensure regulatory compliance.
- Stay abreast of emerging trends, methodologies and technologies in financial AI, machine learning and risk analytics, and evaluate their applicability to our business.
- Optimise data pipelines and analytical infrastructure to enhance processing efficiency, scalability and data quality across analytical systems.
- What we're looking for.
- Master's degree or higher in Data Science, Machine Learning, Statistics, Mathematics, Physics, Computer Science or a related quantitative discipline.
- Minimum 7-10 years of professional experience in data science, machine learning engineering or advanced analytics, with at least 4-5 years specifically focused on financial services, risk analytics or quantitative finance.
- Demonstrated expertise in building and deploying machine learning models in production environments, with proficiency in supervised learning, unsupervised learning, ensemble methods and deep learning techniques.
- Strong proficiency in programming languages such as Python, R or Scala, with experience in relevant libraries and frameworks (e.g. scikit-learn, TensorFlow, PyTorch, XGBoost).
- Solid understanding of financial concepts including credit risk, market risk, operational risk, counterparty risk, regulatory capital requirements and risk measurement methodologies.
- Experience with big data technologies and distributed computing frameworks such as Apache Spark, Hadoop or cloud-based analytics platforms (AWS, GCP, Azure).
- Proven track record of leading analytical projects, mentoring junior staff and driving cross-functional collaboration in a complex organisational environment.
- Strong statistical knowledge including hypothesis testing, causal inference, time series analysis and experimental design.
- Experience with financial data sources, APIs and market data platforms; familiarity with data warehousing and ETL processes is advantageous.
- Excellent communication skills with the ability to present technical concepts clearly to both technical and non-technical audiences.
- Knowledge of financial regulations such as Basel III, IFRS 9, Dodd-Frank or equivalent regional frameworks is highly desirable.
- Certification in machine learning, data science or related disciplines is a plus.
āļāļąāļāļĐāļ°:
Statistics, Big Data, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Formulates advance data analytic, predictive analytic to proactively solve problems and/or create solutions for future business needs (Krungsri Auto App).
- Data Cleansing and Processing-massage and organize data for further advance analytic.
- Correlate disparate datasets.
- Develop new analytical methods and machine learning models (Personalized offering, Event trigger on App).
- Identify new business questions that can add value to organization.
- Conduct causality experiments by applying A/B experiments or any sciences-based approach to identify what best for determined business objectives.
- Discover and create new data features leading to data solutions creation.
- Using data visualization technique and presenting clear outcome as storytelling.
- Apply now if you have these advantages.
- Bachelor degree in Computer Science, Mathematics Science, Statistics Science, Computer Engineering or whoever having confident and ability enough to called self as Data Scientist.
- Must have experience in personalize recommendation and offering for mobile application users.
- 5+ years of experience in Telecom, Banking, Financing and Insurance preferred having 2 industries from the lists.
- 2+ years of experience in Big Data or AI/ML projects.
- 2+ years of experience in Enterprise Data Warehouse and/or data mining.
- Experience in solving business questions projects.
- Strong in Mathematics & Statistics.
- Strong in programming skill: prefer R, Python, SQL.
- Why join Krungsri?.
- As a part of MUFG (Mitsubishi UFJ Financial Group), we a truly a global bank with networks all over the world.
- We offer a striking work-life balance culture with hybrid work policies (3 days in office per week).
- Unbelievable benefits such as attractive bonuses, employee loan with special rates and many more.
- Apply now before this role is close. **.
- FB: Krungsri Career(http://bit.ly/FacebookKrungsriCareer [link removed]).
- LINE: Krungsri Career (http://bit.ly/LineKrungsriCareer [link removed]).
- Talent Acquisition Department.
- Bank of Ayudhya Public Company Limited.
- 1222 Rama III Rd., Bangpongpang, Yannawa, Bangkok 10120.
- āļŠāļāļāļāļēāļĄāļāđāļāļĄāļđāļĨāđāļāļīāđāļĄāđāļāļīāļĄ: Talent Acquisition Center 0-2-----000.
- āļŦāļĄāļēāļĒāđāļŦāļāļļ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļĨāļ°āļāļ°āļĄāļĩāļāļąāđāļāļāļāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāļđāđāļŠāļĄāļąāļāļĢ āļāđāļāļāļāļĩāđāļāļđāđāļŠāļĄāļąāļāļĢāļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļēāļĢāđāļ§āļĄāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāļĻāļĢāļĩāļŊ.
- Remark: The bank needs to and will have a process for verifying personal information related to the criminal history of applicants before they are considered for employment with the bank.
- Applicants can read the Personal Data Protection Announcement of the Bank's Human Resources Function by typing the link from the image that stated below.
- EN (https://krungsri.com/b/privacynoticeen).
- āļāļđāđāļŠāļĄāļąāļāļĢāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāļāļĢāļ°āļāļēāļĻāļāļēāļĢāļāļļāđāļĄāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļŠāđāļ§āļāļāļēāļāļāļĢāļąāļāļĒāļēāļāļĢāļāļļāļāļāļĨāļāļāļāļāļāļēāļāļēāļĢāđāļāđāđāļāļĒāļāļēāļĢāļāļīāļĄāļāđāļĨāļīāļāļāđāļāļēāļāļĢāļđāļāļ āļēāļāļāļĩāđāļāļĢāļēāļāļāļāđāļēāļāļĨāđāļēāļ.
- āļ āļēāļĐāļēāđāļāļĒ (https://krungsri.com/b/privacynoticeth).
āļāļąāļāļĐāļ°:
GMP
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ27,000 - āļŋ40,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļ§āļāļŠāļāļāļāļąāđāļāļāļāļāļ§āļīāļāļĪāļāļīāđāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĨāļīāļ āđāļāđāļ āļāļļāļāļāļ§āļāļāļļāļĄāļŠāļģāļāļąāļ āļĨāļģāļāļąāļāļāļēāļĢāđāļāļīāļĄāļ§āļąāļāļāļļāļāļīāļ āđāļ§āļĨāļē āļāļļāļāļŦāļ āļđāļĄāļī āļāļ§āļēāļĄāđāļĢāđāļ§ āļŦāļĢāļ·āļāđāļāļ·āđāļāļāđāļāļāļ·āđāļāļāļēāļĄāļāļĩāđāļāļģāļŦāļāļ āļāļĢāđāļāļĄāļāļīāļāļāļēāļĄāđāļŦāđāļāļāļąāļāļāļēāļāļāļāļīāļāļąāļāļīāļāļēāļĄāļāļąāđāļāļāļāļāļāļĒāđāļēāļāļāļđāļāļāđāļāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļĢāļ°āļŦāļ§āđāļēāļāļŦāļąāļ§āļŦāļāđāļēāļāļēāļāđāļĨāļ°āļāļāļąāļāļāļēāļāļāđāļēāļĒāļāļĨāļīāļ āđāļāļ·āđāļāļŠāļ·āđāļāļŠāļēāļĢāđāļāļāļāļēāļ āļāđāļāļāļģāļŦāļāļ āļāļąāđāļāļāļāļāļāļēāļĢāļāļģāļāļēāļ āļāļąāļāļŦāļēāļŦāļāđāļēāļāļēāļ āđāļĨāļ°āļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļēāđāļŦāđāđāļāļīāļāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļĢāļāļāļąāļāđāļĨāļ°āļāļģāđāļāļīāļāļāļēāļāđāļāđāļāļĒāđāļēāļāļāđāļāđāļāļ·āđāļāļ.
- āļŠāļāļāļāļēāļāđāļĨāļ°āđāļāļ°āļāļģāļāļāļąāļāļāļēāļāļāđāļēāļĒāļāļĨāļīāļāļāļĩāđāđāļāđāļēāļĄāļēāđāļŦāļĄāđāđāļāļĩāđāļĒāļ§āļāļąāļāļāļąāđāļāļāļāļāļāļēāļĢāļāļĨāļīāļ āļāļēāļĢāđāļāđāļāļēāļāđāļāļāļŠāļēāļĢ āļāļē ...
- āļāļāļāļ§āļāļāļąāļāļāļķāļāļāļąāđāļāļāļāļāļāļēāļĢāļāļĨāļīāļāđāļŦāđāļāļĢāļāļāđāļ§āļ āļāļđāļāļāđāļāļ āļāđāļēāļāđāļāđāļāļąāļāđāļāļ āđāļĨāļ°āļŠāļāļāļāļĨāđāļāļāļāļąāļāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāļāļĢāļīāļ āļĢāļ§āļĄāļāļķāļāļāļĢāļ§āļāļŠāļāļāļāļēāļĢāļĨāļāļāļ·āđāļ āļ§āļąāļāļāļĩāđ āđāļ§āļĨāļē āđāļĨāļ°āļāđāļāļĄāļđāļĨāļāļĩāđāļāļģāđāļāđāļāļāļēāļĄāļāđāļāļāļģāļŦāļāļāļāļāļāđāļāļāļŠāļēāļĢ.
- āļāļāļāļ§āļāļāļąāļāļāļķāļāļāļĨāļāļēāļĢāļāļĨāļīāļāđāļĨāļ°āļāđāļāļĄāļđāļĨāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāļ·āđāļāļĒāļ·āļāļĒāļąāļāļ§āđāļēāļāļĨāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāđāļāđāļāđāļāļāļēāļĄāđāļāļāļāđāļāļĩāđāļāļģāļŦāļāļ āļĢāļ§āļĄāļāļķāļāļāļĢāļ§āļāļŠāļāļāļāļ§āļēāļĄāļŠāļāļāļāļĨāđāļāļāļāļāļāļāđāļāļĄāļđāļĨāļĢāļ°āļŦāļ§āđāļēāļāđāļāļāļŠāļēāļĢāđāļĨāļ°āļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĨāļīāļ.
- āļāļĢāļ§āļāļāļīāļāļāļēāļĄāļāļ§āļēāļĄāļāļĢāđāļāļĄāļāļāļāļāļ·āđāļāļāļĩāđ āđāļāļĢāļ·āđāļāļāļĄāļ·āļ āļāļļāļāļāļĢāļāđ āđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļĩāđāļāļģāđāļāđāļāļāđāļāļāđāļĢāļīāđāļĄāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĨāļīāļ āļāļĢāđāļāļĄāđāļāđāļāļŦāļąāļ§āļŦāļāđāļēāļāļēāļāđāļĄāļ·āđāļāļāļāļāļ§āļēāļĄāļāļīāļāļāļāļāļīāļŦāļĢāļ·āļāļŠāļīāđāļāļāļĩāđāļāļēāļāļŠāđāļāļāļĨāļāļĢāļ°āļāļāļāđāļāļāļļāļāļ āļēāļāđāļĨāļ°āļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒ.
- āļŠāļģāđāļĢāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļĢāļ°āļāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļāļķāđāļāđāļāđāļāļŠāļēāļāļēāļ§āļīāļāļĒāļēāļĻāļēāļŠāļāļĢāđ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļ·āđāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļāļīāļāļąāļāļīāļāļēāļāđāļāđāļĨāļāđāļāļĨāļīāļ āđāļĄāđāļāđāļāļĒāļāļ§āđāļē 2 āļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāļāđāļēāļ āļāļģāļāļ§āļēāļĄāđāļāđāļēāđāļ āđāļĨāļ°āļāļāļīāļāļąāļāļīāļāļēāļĄāđāļāļāļŠāļēāļĢāļ§āļīāļāļĩāļāļāļīāļāļąāļāļīāļāļēāļāļĄāļēāļāļĢāļāļēāļ āđāļāļŠāļąāđāļāļāļĨāļīāļ āđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļ§āļāļāļļāļĄāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāđ.
- āļĄāļĩāļāļ§āļēāļĄāļĨāļ°āđāļāļĩāļĒāļāļĢāļāļāļāļāļ āļĄāļĩāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļ āđāļĨāļ°āļĄāļĩāļāļīāļāļŠāļģāļāļķāļāļāđāļēāļāļāļļāļāļ āļēāļ āđāļāļ·āđāļāļāļāļēāļāļāļģāđāļŦāļāđāļāļāļĩāđāļāđāļāļāļāļĢāļ§āļāļŠāļāļāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļąāđāļāļāļāļāļāļĩāđāļĄāļĩāļāļĨāļāđāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļĨāļīāļāđāļāļĒāļāļĢāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļĢāļ°āļŠāļēāļāļāļēāļ āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļēāļĢāļāđāļēāļĒāļāļāļāļāļ§āļēāļĄāļĢāļđāđāđāļŦāđāđāļāđāļāļāļąāļāļāļēāļāđāļāđāļĨāļāđāļāļĨāļīāļ.
- āļŠāļēāļĄāļēāļĢāļāļāļģāļāļēāļāđāļāđāļāļāļ°āļŦāļĢāļ·āļāļāļģāļāļēāļāļĨāđāļ§āļāđāļ§āļĨāļēāđāļāđāļāļēāļĄāļāļ§āļēāļĄāļāļģāđāļāđāļāļāļāļāđāļāļāļāļēāļĢāļāļĨāļīāļ.
- āļŦāļēāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāđāļēāļāļĢāļ°āļāļāļāļļāļāļ āļēāļ āļāļēāļĢāļāļĢāļ§āļāļŠāļāļāđāļāļāļŠāļēāļĢ āļāļēāļĢāļŠāļāļāļŠāļ§āļāļāļ§āļēāļĄāđāļāļĩāđāļĒāļāđāļāļ āļŦāļĢāļ·āļāļāļēāļĢāļāļķāļāļāļāļĢāļĄāļāļāļąāļāļāļēāļ āļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ.
- āļāļĢāļīāļĐāļąāļāđāļĢāļēāļāļĒāļđāđāļĢāļ°āļŦāļ§āđāļēāļāļāđāļāļŠāļĢāđāļēāļāđāļĢāļāļāļēāļāļĒāļēāđāļāļāļāļąāļāļāļļāļāļąāļāđāļāļ·āđāļāļāļĨāļīāļāļĒāļēāļāļĨāļļāđāļĄ Hormone āđāļĨāļ°āļāđāļāļāļāļēāļĢāđāļ āļŠāļąāļāļĒāļļāļāļāļļāļāđāļāļīāļāļāļĩāđāļāļ° "āļĨāđāļĄāļŦāļąāļ§āļāļĄāļāđāļēāļĒ" āđāļāļāļąāļāļāļĢāļīāļĐāļąāļāđāļāļĨāļąāļāļĐāļāļ°āļĨāļđāļāļŦāļĄāđāļ āđāļāļ·āđāļāļĢāđāļ§āļĄāļāļąāļāļŠāļĢāđāļēāļāđāļĢāļāļāļēāļāļāļĩāđāļĄāļĩāļāļļāļāđāļāđāļāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļāļŦāļāđāļēāđāļāđāļāļāļēāļāļēāļāļĄāļēāļāļĢāļāļēāļ āđāļāđāļĒāļąāļāļāļāļāļĒāļđāđāļāļąāļāļāļ§āļēāļĄāđāļāđāļāļāļĢāļīāļāļ§āđāļē "āļāļāļāđāļāļĒāļāļģāļĢāļąāļāđāļāđāļāļāļāļąāļ§" āđāļāļĢāļēāļ°āđāļāđāļāđāļĢāļāļāļēāļāđāļĢāļ āđāļāļāļāļĩāđāļ§āļēāļāđāļ§āđāļĄāļĩāļāļ§āļēāļĄāđāļāđāļāļāļāļ§āđāļēāļĄāļąāļāļāļ°āđāļĄāđāđāļāđāļāđāļāļāļēāļĄāļāļąāđāļāļāļļāļāļāļĒāđāļēāļ āđāļĄāļ·āđāļāļĄāļĩāđāļāđāļēāļŦāļĄāļēāļĒāđāļāđāļāļāļĢāļĢāļĄāļāļēāļĒāđāļāļĄāļĄāļĩāļāļąāļāļŦāļē āđāļĨāļ°āđāļĢāļēāļāđāļāļāļāļēāļĢāļāļāļāļĩāđāļāļĢāđāļāļĄāļāļĩāđāļāļ°āļāļļāđāļĄāđāļāđāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļē "āđāļāļ·āđāļ" āđāļŦāđāđāļāļāļķāļāđāļāđāļēāļŦāļĄāļēāļĒ.
- āđāļāđāļāļāļāļĄāļĩāļāļ§āļēāļĄāļāļĢāļīāļāđāļ āļāļĢāļīāļāļāļąāļāļāļąāļāļāļēāļĢāļāļģāļāļēāļ āđāļĄāđāđāļŦāđāļāđāļāđāļāļąāļ§ āđāļĨāļ°āđāļĄāđāđāļāđāļāļāļāļĩāđāļĄāļĩ mindset āļāļēāļĢāļāļģāļāļēāļāđāļāđāļāđāļāļ Officer āđāļāđāđāļāđāļāļāļāļāļĩāđāļĄāļĩ Entrepreneur mindset āđāļāđāļēāđāļāļāđāļāļāļģāļāļąāļāļāļēāļāļāļļāļĢāļāļīāļ āļāđāļāļāļģāļāļąāļāļāļēāļāļāđāļēāļāđāļ āļŠāļąāļāļ§āļīāļāļĒāļē āļāđāļāļāļģāļāļąāļāļāļēāļāļāđāļēāļāļāļāļŦāļĄāļēāļĒ āđāļĨāļ° āļŦāļēāđāļŠāđāļāļāļēāļāļāļĩāđāđāļāļāļķāļāđāļāđāļēāļŦāļĄāļēāļĒāđāļāđ (āđāļĄāđāđāļāđāļāļđāļāļāļĩāđāļŠāļļāļ āđāļĄāđāđāļāđāđāļāđāļ°āļāļĩāđāļŠāļļāļ).
- āļāļĄ (āđāļāđāļēāļāļāļ) āđāļāļ·āđāļāļ§āđāļēāļāļļāļāļāļāļĄāļĩāļāļąāļāļ§āđāļēāļ§āļąāļāļāļķāļāļāļĒāļēāļāļāļ°āļāļģāļāļļāļĢāļāļīāļāđāļāđāļāļāļāļāļāļąāļ§āđāļāļ āđāļāđāļāđāļāļĨāļąāļ§āļāļ§āļēāļĄāđāļŠāļĩāđāļĒāļ āđāļāđāļāļģāļāļēāļāļāļĢāļ°āļāļģāđāļāļāļĨāļāļāļāđāđāļĄāđāļŠāļēāļĄāļēāļĢāļāđāļāđāļāļāļīāļŠāļĢāļ°āļāļēāļāļāļēāļĢāđāļāļīāļāđāļāđ āļāđāļēāļāļļāļāļāļ·āļāļāļāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļāļļāđāļĄāđāļāđāļŦāđāļāļąāļāđāļĢāļāļāļēāļāļāļĩāđ āļĄāļĩāļāļ§āļēāļĄāļāļĢāļīāļāđāļ āđāļāđāļāļāļāđāļāļĢāđāļāļāļĩāđāļāļēāļāļāļāļāđāļāļāļļāļāļŠāļĢāļĢāļ āđāļĢāļāļāļāļāļąāļ āđāļĨāļ°āļāļ§āļēāļĄāđāļĄāđāļŠāļĄāļŦāļ§āļąāļāđāļāđ āđāļĄāļ·āđāļāđāļāļĢāļāļāļēāļĢāļŠāļģāđāļĢāđāļāļāļāļāļāļēāļāđāļāļīāļāđāļāļ·āļāļāđāļĨāļ° Bonus āļāļēāļāļāļĢāļīāļĐāļąāļāļāļ°āđāļāđāļāļŦāļļāđāļāđāļāđāļĢāļāļāļēāļāđāļŦāđāđāļĨāļ°āļāđāļēāļāļāļ°āļŠāļēāļĄāļēāļĢāļāđāļāđāļĢāļąāļāļāļąāļāļāļĨāđāļāļīāļāđāļāđāļāļāļĢāđāļāļĄāļāļąāļāļāļĢāļīāļĐāļąāļ.
- āļāļļāļāļĨāļīāļāļāļāļāļāļāļāļĩāđāļāđāļāļāļāļēāļĢ.
- āđāļāđāļāļāļāļŦāļāļąāļāđāļāđāļ āđāļĄāđāđāļĨāđāļĨ āđāļĄāļ·āđāļāļāļąāļāļŠāļīāļāđāļāļĄāļēāļāļģāļāļēāļāļāļĩāđāļāļĩāđāđāļĨāđāļ§āļāđāļĨāļ Resume āļāļąāļ§āđāļāļāļāļēāļāļŠāļēāļĢāļāļ āļāļĢāđāļāļĄāļāļļāđāļĄāđāļŦāđāļāļąāļāļāļēāļāļāļĒāđāļēāļāđāļāđāļĄāļāļĩāđ.
- āđāļāđāļāļāļāļāļĨāļēāļ āļāļĩāđāļāļēāļĢāļĄāļāđāļāļĩ.
- āđāļāđāļāļāļāļāļĩāđāļŠāļ·āđāļāļŠāļēāļĢāļāļąāļāļāļĩ āđāļĄāļ·āđāļāļŠāļāļŠāļąāļĒāļŦāļĢāļ·āļāļĄāļĩāļāļģāļāļēāļĄ.
- āļĄāļāļāļ āļēāļāļ§āđāļēāļāļąāļ§āđāļāļāļāļ°āļāļĒāļđāđāļāļĩāđāļāļĩāđāļāļĒāđāļēāļāļāđāļāļĒ 5āļāļĩ.
- Culture āļāļēāļĢāļāļģāļāļēāļāđāļāļāļĢāļīāļĐāļąāļ.
- āđāļāđāđāļĄāļāđ āđāļĄāđfake.
- āļāļĨāđāļēāļāļąāļāđāļāđāļĄāļ·āđāļāļāđāļāļāļāļĢāļ°āļŦāļēāļĢ āđāļāđāļāļ·āļāļāļāđāļāļāđāļāđāđāļĄāļ·āđāļāļāļģāđāļāđāļ āļāļāļĄāļĒāļļāļāļāđāļĒāļēāļĄāļāļģāđāļāđāļāļāđāļāļāļāļĨāđāļēāļāļąāļāļāļĢāļ°āļāļĩāđ (āļāļāļāđāļāđāļāļ·āļāļāļĨāļđāļāļāđāļāļ āļāļĩāļāļ§āđāļēāļĒāļīāđāļĄāļāđāļāļŦāļāđāļē āļāļđāļāļ§āđāļēāđāļĄāđāđāļāđāļāđāļĢ āđāļāđāđāļāļāļīāļāļāļēāļĨāļđāļāļāđāļāļāļĨāļąāļāļŦāļĨāļąāļ āļāļąāļāļāļąāđāļāļāļ·āļāļŦāļąāļ§āļŦāļāđāļēāļāļĩāđāļāļĩāđāļāļĨāļēāļ).
- āđāļĄāđāļĒāļāļāļāļāđāļĄāļāđāļēāļ āđāļāļāļāļāļāđāļāļāđāļĒāļ āđāļĢāļēāļĒāļīāđāļāļāđāļāļāļāđāļāļāļāļ§āđāļē āđāļāđāļāļąāļāļāļāđāļĄāđāļāļĩ āļĒāļāļĄāļŦāļąāļāđāļĄāđāļĒāļāļĄāļāļ.
- āļāļļāļĒāļāļąāļāļāđāļ§āļĒāđāļŦāļāļļāļāļĨāđāļĨāļ°āļāļąāļ§āđāļĨāļ āđāļāđāļŦāđāļēāļĄāļĄāļāļāļāļāđāļāđāļāļāļąāļ§āđāļĨāļ āļāļāļĄāļĩāļŦāļąāļ§āđāļ āļāļ§āļēāļĄāļĢāļđāđāļŠāļķāļ āļāļ§āļēāļĄāļāļđāļāļāļąāļ āļāļĩāđāļāļļāļāļ§āļąāļāļāļĩāđāļĒāļąāļāđāļĄāđāļĄāļĩāđāļāļĢāļāļģāļāļ§āļāđāļāđ.
- āļĒāļāļĄāļĨāļēāļāļāļāđāļāļāļēāļĒāļāļēāļāļŦāļāđāļē āļāļĩāļāļ§āđāļēāļāļĒāļđāđāđāļāļāļāļāđāļāļĢāļāļĩāđāļĄāļĩāļāļĨāļąāļāļāļēāļāđāļāļ·āđāļāļĒ (āđāļāđāļ āļāļąāđāļāļāļąāļāļ§āļąāļāļĨāļēāļāđāļ§āļĒāļ§āđāļē āļāļĩāļāļĩāđāļāđāļ§āļĒāļāļĢāļāļŠāļīāļāļāļīāđāļŦāļĢāļ·āļāļĒāļąāļ). āļāļĩāļāļķāļāļāļāļŦāļĄāļēāļĒāđāļŦāđāļāđāļ§āļĒ 30āļ§āļąāļ āđāļāļāļāļāđāļāļ·āđāļāļĒ āļāļĩāļāļķāļāļāļģāļāļēāļ 250āļ§āļąāļ āļāļĩāđāđāļ āļāļąāļāđāļ 30/250 (12%. āđāļāđāđāļĄāđāđāļāđāļē āđāļāļāļĢ Toxic).
- āđāļāļīāļāđāļāđāļĄāļĩāđāļāļāļāđāļāļģāļāļ§āļāđāļāđāļāļĢāļ°āļĒāļ°āļāļēāļāđāļāđ Cover āđāļāđāļāļāļ.
- āļŠāļāļēāļāļāļĩāđāļāļģāļāļēāļāļŦāļĨāļąāļ āđāļĢāļāļāļēāļāļāļĩāđāļāļēāļāļāļĨāļĩ āļŠāļĄāļļāļāļĢāļāļĢāļēāļāļēāļĢ.
- āļāļĢāļ°āļāļąāļāļŠāļļāļāļ āļēāļ OPD 3,000āļāļēāļ - āļāļģāđāļĄāļāđāļāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđ: āđāļĄāđāļĄāļĩāđāļĒāļāļāļĢāļ°āļāļąāļāļĢāļ°āļŦāļ§āđāļēāļāļāļģāđāļŦāļāđāļāđāļāļīāļāđāļāļ·āļāļāļŠāļđāļāļŦāļĢāļ·āļāļāđāļģ āđāļāļĢāļēāļ°āļāļāđāļāļīāļāđāļāļ·āļāļāļŠāļđāļāļĄāļĩāđāļāļīāļāļŦāļēāļŦāļĄāļāļāļĒāļđāđāđāļĨāđāļ§ āđāļĢāļ·āđāļāļāļŠāļļāļāļ āļēāļāļāļ§āļĢāļāļ°āđāļāđāļēāļāļąāļ.
- āļāļĢāļ°āļāļąāļāļŠāļļāļāļ āļēāļ IPD āđāļĒāļāļ°āļāļĒāļđāđāļāļĢāļąāļ āđāļāđāļāļģāđāļĄāđāđāļāđāļ§āđāļēāđāļāđāļēāđāļŦāļĢāđ.
- āļāļģāđāļĄāļāđāļāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđ: Concept āđāļāđāļāđāļāļ§āļāļĢāļ°āļĄāļēāļāļ§āđāļēāļāļĨāļąāļ§āļāļāļąāļāļāļēāļāļāļąāļ§āđāļĨāđāļāđāļāļ°āđāļāđāļāļŦāļāļĩāđāļāđāļēāļāļĢāļ°āļŠāļāļāļļāļāļąāļāļīāđāļŦāļāļļāļŦāļāļąāļāļāļķāđāļāļĄāļēāđāļĨāļĒāļāļģ IPD āđāļŦāđ Cover āđāļ§āđ āđāļāđāļāļāļāđāļāļīāļāđāļāļ·āļāļ 15,000 āđāļŦāļĨāļ·āļāđāļāđāļ 2,000 āļĨāđāļĄāļĄāļāđāļāļāļĢāđāđāļāļāđāđāļāļāļŦāļąāļāļĄāļēāļāđāļēāļāđāļāļāļāđāļēāļĒ 50,000 āļāļĩāļ§āļīāļāđāļāļēāļāđāļāļąāļāđāļĨāđāļ§.
- Bonus 1-3āđāļāļ·āļāļ.
- āļāļĢāļąāļāđāļāļīāļāđāļāļ·āļāļāļāļĩāļĨāļ°āļŦāļāļķāđāļāļāļĢāļąāđāļ(1%-5%).
- āļāđāļāļāđāļĄāđāđāļāđāļĢāļąāļāđāļāđāļāļ·āļāļ āđāļĨāļ°āļāļĨāļāļēāļĢāļāļģāļāļēāļāļāđāļāļāđāļāđāļāļāļĩāđāļāļāđāļāļāļāļāļāļđāđāļāļąāļāļāļąāļāļāļąāļāļāļēāļāđāļāļ°āđāļāđāļēāđāļāļāļāđāļāļēāļĢāļąāļāļāļĩ Bonus 1āđāļāļ·āļāļ, āļŠāđāļ§āļāļāđāļāļāđāļāļĩāđāļāļāđāļŦāļĄāđ āđāļāđāļĢāļāđāļĄāđāļāđāļĄāđāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ āđāļāđāļāļģāļāļēāļāđāļāđāļāđāļāļ·āđāļāđāļŦāđāđāļŦāđāļāļ§āđāļēāļāļĢāļīāļĐāļąāļāļāļāļāļāļāļŠāļđāđāļāļēāļ āļāļāđāļāđāļĢāļ āđāļĢāļēāļĒāļīāļāļāļĩāļĄāļāļ Bonus āļŠāļđāļāļŠāļļāļ 3āđāļāļ·āļāļāđāļŦāđāļŠāļģāļŦāļĢāļąāļāļāļđāđāđāļāđāļ Star āđāļĨāļ°āļŦāļēāļāļĄāļĩāļāļēāļĒāļļāļāļēāļāđāļāļīāļ 5āļāļĩ āļāđāļāļēāļāđāļāđāļĢāļąāļ Bonus āļŠāļđāļāļŠāļļāļāļāļķāļ6āđāļāļ·āļāļ āđāļĨāļ° Incentive āļāļ·āđāļāđāļāļĩāļāļĄāļēāļāļĄāļēāļĒ āđāļāđāļŦāļēāļāđāļāđāļāļāļāļąāļāļāļēāļāđāļāđāļēāļāļēāļĄāđāļĒāđāļāļāļēāļĄāđāļĢāļēāļāđāļāļāļāđāļāļāđāļĒāļāļāļēāļāļāļąāļāđāļĄāđāļāđāļēāļāđāđāļĢāđāļ§.
- āļāļđāđ Vending āļāļģāļŦāļāđāļēāļĒāļāđāļģāļāļāļĄ āļĨāļ50% āļāļēāļāļĢāļēāļāļē 7-11 - āļāļģāđāļĄāļāđāļāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđ: āļāđāļēāļāđāļāļāļāļāļąāļāļāļēāļāļāļąāļ§āđāļĨāđāļāđ āđāļāļīāļāđāļāļ·āļāļāđāļĄāđāļĄāļēāļ āļāļĢāļāļāļīāļāļ·āđāļāļāļāļĄāļāļīāļāļ§āļąāļāļĨāļ° 50āļāļēāļ āļāļĢāļ°āļŦāļĒāļąāļāđāđāļāđ 25āļāļēāļ āđāļāļ·āļāļāļāļķāļāļāļģāļāļēāļ 25āļ§āļąāļāļāđ 625āļāļēāļ āļāļĢāļāļāļīāđāļāđāļāđāļāđ 2,000āļāđāļāđāļāļ·āļāļ 625 āļāļēāļāļāđāđāļāđāļāđāļāļīāļ 30% āļāļāļāđāļāļīāļāđāļāđāļāđāļĨāđāļ§.
- āļāļēāļŦāļēāļĢāđāļŠāļĢāļīāļĄāļŠāđāļ§āļāļāļĨāļēāļāļāļāļ Fitwhey (āļāđāļēāļāļāļāļīāļāđāļ§āļĒāđāļāļĢāļ°āļŦāļĒāļąāļāđāļāļ·āļāļāļĨāļ°āđāļāđāļāļāļąāļāđāļāđāļāļāļ) - āļāļģāđāļĄāļāđāļāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđ: Whey Protein āđāļĄāđāđāļāđāđāļāļĢāļāļĩāļāđāļĢāđāļāļāļĨāđāļēāļĄāđāļāđāđāļāđāļ Super Food āļāļĩāđāļĄāļĩ Health benefit āļĄāļēāļāļĄāļēāļĒ.
- CodeāļŠāđāļ§āļāļĨāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļēāļŦāļēāļĢāđāļŠāļĢāļīāļĄ - āļāļģāđāļĄāļāđāļāļāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđ: āđāļŦāļĄāļ·āļāļāļāđāļ4.
- āļāļāļāļāļģāļĨāļąāļāļāļēāļĒāļāļĒāđāļēāļāļāđāļāļĒāļāļēāļāļīāļāļĒāđāļĨāļ° 2 āļ§āļąāļ āđāļāļĒāļĄāļĩ Trainer / āļāļĢāļđYoga āļĄāļēāļŠāļāļāđāļŦāđāļāļĢāļĩ - āļāļģāđāļĄāļāđāļāļāļāļāļāļāļģāļĨāļąāļāļāļēāļĒ: Fitwhey āđāļāđāļ Sport Nutrition āļāļąāļāļāļąāļāļŦāļāļķāđāļāļāļāļāđāļĄāļ·āļāļāđāļāļĒ āđāļĢāļēāļāļēāļĒāļāļēāļŦāļēāļĢāđāļŠāļĢāļīāļĄāđāļŦāđāļāļąāļāļāļĩāļŽāļēāđāļŦāđāļāļāļāļąāđāļ§āļāļĢāļ°āđāļāļĻ āđāļĨāļ°āđāļāđāļāļĒāđāļģāļ§āđāļēāđāļāđāļāđāļ§āļĒāđāđāļāļĢāļāļĩāļāļāļĩāđāļāđāļāļāđāļāđāļāļ§āļāļāļđāđāļāļąāļāļāļēāļĢāļāļāļāļāļģāļĨāļąāļāļāļēāļĒ āļāļąāļāļāļąāđāļāļāļāļąāļāļāļēāļāļāļāļāđāļĢāļēāļāđāļāļ§āļĢāļĄāļĩāļĢāđāļēāļāļāļēāļĒāļāļĩāđāļāļīāļ (Walking the talk) āļāļļāļāđāļĄāđāļāđāļāļāļāļīāļāļĢāļ°āļāļąāļāđāļĨāļ āđāļāđāđāļāļ§āļąāļāļāļĩāđāļāļļāļāļāđāļēāļ§āļāļēāđāļāđāļēāļĄāļēāļāļģāļāļēāļāļāļĩāđāļāļīāļāđāļ§āļĒāđ āļāļļāļāļāļ°āļāđāļāļāļāļīāļāļāļ§āđāļēāļāļāļāļāļĩāđāļāļģāļāļēāļāļāļĒāļđāđāļāļĩāđāļāļ·āđāļ āļāļļāļāļāļ°āļāđāļāļāđāļāđāļ TheBestVersion āļāļāļāļāļąāļ§āđāļāļāļāļĩāđāļāļīāļāđāļ§āļĒāđ āđāļāļĢāļēāļ°āđāļāļ·āđāļāļāđāļāļĩāđāđāļāđāļāļāđāļāļĩāđāļāļīāļāđāļ§āļĒāđāļāļ°āļāļ§āļāļāļąāļāļāļīāļāđāļāļāđāļ§āļĒāļāļąāļ.
- āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩ āļ§āļąāļāļĨāļēāļāļīāļ āļĨāļēāļāđāļ§āļĒ (āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩāļĄāļēāļāļāļ§āđāļēāļāļāļŦāļĄāļēāļĒāļāļģāļŦāļāļ āđāļāđāļāđāļēāđāļāđāļāļāļāļāļģāļāļēāļāļāļĢāļīāļāđ everyday is working day and must work from anywhere āļāļĢāļąāļ) āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩāļāļ§āļāļāļĩāđāļĄāļĩāđāļ§āđāđāļŦāđāļāđāļāļ Operation āđāļāđāļāļĨāļąāļāļāđāļēāļāđāļĨāļ°āļāļąāļāļāđāļāļ āđāļāđāļāđāļēāļŦāļąāļ§āļŦāļāđāļēāđāļŦāđāļĄāļāļāļ§āđāļēāļĄāļąāļāļāļ·āļāļ§āļąāļāļāļąāđāļ Clear āļāļēāļ.
- āļāļĢāļ°āļāļąāļāļŠāļąāļāļāļĄ.
- VeryVeryFastPace āđāļāđāļāļāļāļāļīāļāđāļĢāđāļ§ āļāļģāđāļĢāđāļ§.
- āļŦāļēāļāļēāļāđāļāļ·āđāļāļāļģāļāļēāļ āđāļĄāđāđāļāđ āļāļģāļāļēāļāđāļāļ·āđāļāļŦāļēāđāļāļīāļ.
- WorkLife.
- āļāļģāļāļēāļāđāļāļāđāļĄāđ PlaySafe.
- āļŦāļāļąāļāļāđāļ§āļĒāļāļąāļ āđāļŦāļāļ·āđāļāļĒāļāđāļ§āļĒāļāļąāļ āđāļĄāđāļāļīāđāļāļāļąāļ, āļāļēāļāļļāđāļāđāļāļ āđāļŦāđāļāļāļąāļāļāļāļāļĨāļģāļāļēāļ.
- āļĄāļĩ Org chart āđāļāđāļāļāļąāļāļāļēāļāļāļļāļĒāļāļąāļāđāļāđāļāļļāļ Layer āđāļĄāđāđāļāđāļēāļĒāļĻ āđāļāđāļēāļāļĒāđāļēāļ, āļāļģāļāļēāļāļĄāļĩāđāļ§āđāđāļāļ·āđāļāļŠāļąāđāļ āļāļąāļāļŠāļīāļāđāļ āđāļĨāļ°āļĢāļąāļāļāļīāļāļĢāļąāļāļāļāļ āđāļĄāđāđāļāđāļĄāļĩāđāļ§āđāđāļĨāđāļāļāļēāļĢāđāļĄāļ·āļāļ.
- āļāļāļāļĨāļēāļāļ§āļīāļāļēāļāļēāļĢ āđāļāđāļāļāļāļķāļāļāļāļāļđāđāļāđāđāļāļāļķāļ.
- āđāļĄāđāļĒāļāļĄāđāļāđāļāļ°āđāļĢāļāđāļēāļĒāđ āļāļ°āđāļĢāļāļĩāđāļāļāļāļđāļāļ§āđāļēāđāļĄāđāđāļāđ āļĒāļīāđāļāļāđāļāļāļāļģāļĄāļąāļāđāļŦāđāđāļāđ.
- EQ āļāļĩ.
- Fuck āļ§āļļāļāļīāļāļēāļĢāļĻāļķāļāļĐāļē āļāļēāļĢāļĻāļķāļāļĐāļēāđāļāđāļāļĢāļīāļāļāļĒāļđāđāļāļĩāđāļāļĨāļēāļĒāļāļīāđāļ§ āļ§āļąāļāļāļĩāđāļāļĒāļēāļāđāļĢāļĩāļĒāļ Calculus āļāđāđāļĢāļĩāļĒāļāđāļāđ āļāļĢāļļāđāļāļāļĩāđāļāļ°āđāļāļĨāļĩāđāļĒāļāđāļāđāļ Digital marketing āļāđāļāļģāđāļāđ.
- āļāļĨāđāļēāļāļīāļ āļāļĨāđāļēāļāļģ āļāļģāļāļēāļāđāļāļāđāļĄāđāļāļĨāļąāļ§āđāļāļāļāļēāļĒāļāđāļē.
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļāļīāļāļāļ·āđāļāļāļēāļāđāļāđāļ āļāļĩāļāļāļāļīāļ, āļāļ§āļēāļĄāļāđāļēāļāļ°āđāļāđāļ, āļŠāļāļīāļāļī, Log/āđāļĨāļāļĒāļāļāļģāļĨāļąāļ, Calculus āđāļāļĢāļ°āļāļąāļāđāļāļ·āđāļāļāļāđāļ.
- āļŠāļēāļĄāļēāļĢāļāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄāđāļāļ āļēāļĐāļēāđāļāļāđāđāļāđāļŦāļĢāļ·āļāđāļāđāļāļāļđāđāđāļāđ Google Sheet (Excel) āļĢāļ°āļāļąāļāļŠāļđāļ.
- āļĄāļĩāļāļĨāļąāļāļāļēāļāļāļ§āļ āđāļĄāđāļāļīāļāļĨāļ āļāļ§āļēāļĄāđāļāđāļāļĨāļđāļāļāļđāđāļāļēāļĒ āđāļāļīāļ 100%.
āļāļąāļāļĐāļ°:
Project Management, Data Analysis, Power BI, Python, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Create project implementation plans and follow up with related departments.
- Perform data analysis using statistical tools, scientific methods, and machine learning.
- Develop data applications and ensure validation, testing, monitoring, and documentation.
- Coordinate with internal and external stakeholders to support digital transformation projects.
- Degree in Computer Science, Data Science, or related fields.
- Knowledge of Manufacturing processes is preferred.
- Experience in Data Science or data analytics.
- Skills in Python, SQL, Git, advanced statistics, data transformation, and Power BI.
- Good English communication skills.
- Required Skills.
- Python.
- SQL.
- Git.
- Advanced statistics.
- Data access and data transformation.
- Power BI.
- Machine learning and data analytics knowledge.
- Competencies.
- Strong result orientation and future-oriented mindset.
- Good cooperation and communication skills.
- Ability to coordinate with cross-functional teams and external partners.
- Strong depth of technical and methodological knowledge.
- Interest in digital transformation and i4.0 initiatives.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
1 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistical Analysis, Project Management, Product Design, Enthusiastic, Recruitment
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Experience with any purchase /receipt capture methodologies and experience FMCG market will be a fantastic addition.
- Strong stakeholder management and project management skills together with agile, solution-oriented ways of working and a high interest in decision making to bring our product forward are driving you.
- Within the PDE group, the profound coding experience and technical skills combined with a flexible and innovative mindset are essential skills.
- Education & Experience.
- University degree in Statistics, Mathematics, Socio-economics, or a related field.
- Minimum of 1-2 years of experience in FMCG, Retail, Consumer Research, or a related industry.
- Familiarity with the APAC market, ideally located near our Bangkok office.
- Skills & Knowledge.
- Master's degree Graduated.
- Strong understanding of consumer behavior, and interest in any purchase/receipt collection methodologies.
- Candidates with Panel consumer experience are welcome.
- Proficiency in statistical analysis and handling large data sets.
- Working knowledge of Python (required) and R (optional).
- Ability to extract, clean, and analyze data; strong visualization and root cause analysis capabilities.
- Experience with SQL and large-scale databases.
- Proficient English skills for cross-country collaboration and stakeholder engagement.
- Effective project management and time management skills.
- Ability to extract, clean, and analyze data; strong visualization and root cause analysis capabilities.
- Comfortable working in agile environments and using agile tools.
- Skilled at navigating complex challenges and maintaining collaborative, solution-driven communication.
- Your Personal Qualities.
- Reliable and results-driven with a strong "Say-Do" ratio.
- Open communicator with a collaborative and positive approach.
- Adaptable, proactive, and enthusiastic about working in a global, dynamic team.
- If you're passionate about data, consumer insight, and continuous product improvement and enjoy collaborating across cultures and disciplines we'd love to hear from you.
- We are a global, diverse team that is passionate about bringing world-class solutions alive and engaging with different end2end business branches to bring the complete consumer journey to our clients. Client satisfaction and highest quality standards in the markets across the world are our core drivers and main focus areas.
- Our Benefits.
- Flexible working environment.
- Volunteer time off.
- LinkedIn Learning.
- Employee-Assistance-Program (EAP).
- NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including rÃĐsumÃĐ screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ's principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ's AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/.
- About NIQ.
- NIQ is the world's leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights delivered with advanced analytics through state-of-the-art platforms NIQ delivers the Full View . NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world's population.
- For more information, visit NIQ.com.
- Want to keep up with our latest updates?.
- Follow us on: LinkedIn | Instagram | Twitter | Facebook.
- Our commitment to Diversity, Equity, and Inclusion.
- At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion.
āļāļąāļāļĐāļ°:
Data Analysis, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Build and maintain datasets optimized for analytics and modeling.
- Collaborate closely with Data Engineers for data preparation and with Engineer teams for model deployment and monitoring.
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
- Experience developing machine learning or statistical models for business problems.
- Strong programming skills in Python or similar languages for data analysis and modeling.
- Experience with data analysis, feature engineering, and model evaluation.
- Familiarity with ML frameworks and data visualization tools.
- Understanding of model deployment and monitoring concepts.
āļāļąāļāļĐāļ°:
Recruitment, Leadership Skill, YouTube, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Identify and solve high-leverage problems across Agoda.
- Translate ambiguous problems into measurable objectives and technical solutions.
- Lead the design and deployment of ML, AI, and analytical solutions.
- Own solutions from problem definition and experimentation through production deployment, measurement, and iteration.
- Partner with cross-functional leaders to shape strategy and roadmaps.
- Provide technical leadership and mentorship across the data-science community.
- What You'll Need.
- Extensive hands-on experience in data science, machine learning, and statistics.
- Experience delivering production models with measurable customer or business impact.
- Strong Python and SQL skills; experience with large-scale data and ML platforms.
- Ability to influence senior stakeholders and communicate clearly across technical and non-technical audiences.
- Nice to Have.
- Experience in online travel, e-commerce, search, ranking, recommendations, pricing, or personalization.
- Experience with production LLM or generative-AI systems.
- Advanced degree in Computer Science, Statistics, Operations Research, Mathematics, or a related field.
- Please review our Hiring Process Guidelines before your interview click here to learn how interviewing at Agoda works.
- Discover more about working at Agoda.
- Agoda Careers https://careersatagoda.com.
- Facebook https://www.facebook.com/agodacareers/.
- LinkedIn https://www.linkedin.com/company/agoda.
- YouTube https://www.youtube.com/agodalife.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- Disclaimer.
- We do not accept any terms or conditions, nor do we recognize any agency's representation of a candidate, from unsolicited third-party or agency submissions. If we receive unsolicited or speculative CVs, we reserve the right to contact and hire the candidate directly without any obligation to pay a recruitment fee.
āļāļąāļāļĐāļ°:
Big Data, Power BI, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Modeling & Machine Learning: āļāļąāļāļāļē āļāļāļŠāļāļ āđāļĨāļ°āļāļģ Machine Learning Models āđāļāđāļāđāļāļēāļāļāļĢāļīāļ (Production) āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļēāđāļĨāļ°āļŠāļĢāđāļēāļāđāļāļāļēāļŠāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļ Sales Forecasting, Optimization, Fraud Detection āđāļĨāļ° Use Cases āļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- Advanced Analytics: āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļĒāđāļāđāđāļāļāļāļīāļāļāļēāļāļŠāļāļīāļāļīāļāļąāđāļāļŠāļđāļāđāļĨāļ° Algorithms āđāļāļ·āđāļāļāđāļāļŦāļēāļĢāļđāļāđāļāļ (Patterns) āļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđ āđāļĨāļ°āļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļāļāļēāļāļāđāļāļĄāļđāļĨāļāļāļēāļāđāļŦāļāđ (Big Data) āļāļąāđāļāđāļāđāļāļąāđāļāļāļāļ Data Exploration āđāļāļāļāļāļķāļ Advanced Analytics.
- Dashboard & Reporting: āļāļąāļāļāļē āļāļđāđāļĨ āđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļ Dashboard āļāđāļ§āļĒ Power BI āđāļāļ·āđāļāđāļāđāļāļīāļāļāļēāļĄ ...
- Business Analysis: āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļ·āđāļāļāđāļāļŦāļē Insights āđāļāļ§āđāļāđāļĄ (Trends) āđāļāļāļēāļŠāļāļēāļāļāļļāļĢāļāļīāļ āđāļĨāļ°āļŠāļēāđāļŦāļāļļāļāļāļāļāļąāļāļŦāļē āđāļāļ·āđāļāļŠāļāļąāļāļŠāļāļļāļāļāļļāļĢāļāļīāļāļāđāļēāļ āđ āļ āļēāļĒāđāļ BCP Group āđāļāđāļ Marketing, Refinery, Solar, Biofuel āđāļĨāļ°āļāļļāļĢāļāļīāļāļāļ·āđāļ āđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- Data Interpretation & Business Insights: āđāļāļĨāļāļĨāļāđāļāļĄāļđāļĨāđāļĨāļ°āļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāđāđāļāđāļāļāđāļāđāļŠāļāļāđāļāļ°āļāļĩāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļāļāļīāļāļąāļāļīāđāļāđ (Actionable Insights) āđāļāļ·āđāļāđāļāļīāđāļĄāļĢāļēāļĒāđāļāđ āļĨāļāļāđāļāļāļļāļ āļŦāļĢāļ·āļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļāļāļāļļāļĢāļāļīāļ āđāļāđāļ āđāļĢāļāļāļĨāļąāđāļāļāđāļģāļĄāļąāļ āļŠāļāļēāļāļĩāļāļĢāļīāļāļēāļĢ āđāļĨāļ°āļāļļāļĢāļāļīāļāļāļ·āđāļ āđ.
- Insight Communication: āļāļąāļāļāļģ Data Visualization āđāļĨāļ°āļāļģāđāļŠāļāļāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĩāđāļāļąāļāļāđāļāļāđāļŦāđāļāļĒāļđāđāđāļāļĢāļđāļāđāļāļāļāļĩāđāđāļāđāļēāđāļāļāđāļēāļĒ āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāļąāļāļāļđāđāļāļĢāļīāļŦāļēāļĢāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļāļļāļĢāļāļīāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ āđāļāļĒāļŠāļēāļĄāļēāļĢāļāđāļāđ Power BI āđāļāļāļēāļĢāļāļģāđāļŠāļāļāļāđāļāļĄāļđāļĨāđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- Business Value Delivery: āļŠāļēāļĄāļēāļĢāļāđāļāļ·āđāļāļĄāđāļĒāļāļāļĨāļāļēāļāļāđāļēāļ Data & Analytics āđāļŦāđāđāļāļīāļ Business Value āļāļĩāđāļ§āļąāļāļāļĨāđāļāđ āđāļĨāļ°āļĢāļąāļāļāļīāļāļāļāļ KPI āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļļāļāļāđāļēāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļ Revenue Enhancement, Cost Saving, Productivity Improvement āļŦāļĢāļ·āļ Efficiency Improvement.
- āļāļĢāļīāļāļāļēāļāļĢāļĩ/āđāļ āļŠāļēāļāļēāļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ, āļŠāļāļīāļāļī, āļāļāļīāļāļĻāļēāļŠāļāļĢāđ, āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāļāđāļēāļ Data Analytics/Business Intelligence 1-3 āļāļĩ.
- āļāļąāļāļĐāļ°āļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄ Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) āļāļĒāđāļēāļāļāļĩ.
- āđāļāļĩāđāļĒāļ§āļāļēāļāļāļēāļĢāđāļāđ SQL āļŠāļģāļŦāļĢāļąāļāļāļķāļāļāđāļāļĄāļđāļĨāđāļāļĢāļ°āļāļąāļāļŠāļđāļ.
- āļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđāđāļāļĢāļ·āđāļāļāļĄāļ·āļ Visualization (Power BI).
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļĢāļīāļāļāļāļēāļāļāļļāļĢāļāļīāļ (Business Acumen) āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāđāļāļĄāļđāļĨāļāļĩāđāļāļąāļāļāđāļāļāđāļāđāļāļĩ.
- āļāļĪāļāļīāļāļĢāļĢāļĄāđāļĨāļ°āļāļļāļāļĨāļąāļāļĐāļāļ°āļāļĩāđāļāļēāļāļŦāļ§āļąāļ (Behavioral Competencies).
- Collaboration & Teamwork: āļĄāļĩāļāļąāļĻāļāļāļāļīāđāļāļāļēāļĢāļāļģāļāļēāļāđāļāđāļāļāļĩāļĄ āļāļĢāđāļāļĄāđāļŦāđāļāļ§āļēāļĄāļĢāđāļ§āļĄāļĄāļ·āļāđāļĨāļ°āļŠāļāļąāļāļŠāļāļļāļāđāļāļ·āđāļāļāļĢāđāļ§āļĄāļāļēāļāđāļāļ·āđāļāđāļŦāđāļāļĩāļĄāļŠāļēāļĄāļēāļĢāļāļāļĢāļĢāļĨāļļāđāļāđāļēāļŦāļĄāļēāļĒāļĢāđāļ§āļĄāļāļąāļ.
- Business Mindset: āļŠāļēāļĄāļēāļĢāļāļĄāļāļāļāļēāļāļāđāļēāļ Data & Analytics āđāļāļĄāļļāļĄāļĄāļāļāļāļāļāļāļļāļĢāļāļīāļ āđāļĨāļ°āļĄāļļāđāļāđāļāđāļāļāļēāļĢāļŠāļĢāđāļēāļāļāļĨāļĨāļąāļāļāđāļāļĩāđāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāđāļāđāļāļĢāļ°āđāļĒāļāļāđāđāļĨāļ°āļŠāļĢāđāļēāļāļāļļāļāļāđāļēāđāļŦāđāļāļąāļāļāļāļāđāļāļĢāđāļāđāļāļĢāļīāļ.
- Problem Solving: āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļąāļāļŦāļēāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ āļŠāļēāļĄāļēāļĢāļāļāđāļāļŦāļēāļŠāļēāđāļŦāļāļļāđāļĨāļ°āļāļģāļāđāļāļĄāļđāļĨāļĄāļēāđāļāđāđāļāļāļēāļĢāļāļąāļāļāļēāđāļāļ§āļāļēāļāđāļāđāđāļāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄ.
- Communication: āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāđāļāļĄāļđāļĨāđāļāļīāļāđāļāļāļāļīāļāđāļĨāļ°āļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļŦāđāļāļđāđāļāļĩāđāđāļĄāđāļĄāļĩāļāļ·āđāļāļāļēāļāļāđāļēāļ Data āđāļāđāļēāđāļāđāļāđāļāļĒāđāļēāļāļāļąāļāđāļāļ.
- Ownership & Flexibility: āļĄāļĩāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļāļāđāļāļāļēāļ āļĄāļĩāļāļ§āļēāļĄāļĒāļ·āļāļŦāļĒāļļāđāļ āđāļĨāļ°āļāļĢāđāļāļĄāļŠāļāļąāļāļŠāļāļļāļāļāļēāļāđāļāļāđāļ§āļāđāļ§āļĨāļēāļāļĩāđāļāļģāđāļāđāļāđāļāļ·āđāļāđāļŦāđāļāļēāļāļŦāļĢāļ·āļāđāļāļĢāļāļāļēāļĢāļŠāļēāļĄāļēāļĢāļāļāļģāđāļāļīāļāļāļēāļĢāđāļāđāļāļēāļĄāđāļāđāļēāļŦāļĄāļēāļĒ.
- āļĨāļąāļāļĐāļāļ°āđāļĨāļ°āđāļāļ·āđāļāļāđāļāļāļēāļĢāļāļģāļāļēāļ (Working Conditions).
- āļŠāļēāļĄāļēāļĢāļāļāļāļīāļāļąāļāļīāļāļēāļāļāļĢāļ°āļāļģāļŠāļģāļāļąāļāļāļēāļāļāļēāļĄāđāļ§āļĨāļēāļāļģāļāļēāļāļāļāļāļāļĢāļīāļĐāļąāļ 5 āļ§āļąāļāļāđāļāļŠāļąāļāļāļēāļŦāđ.
- āļŠāļēāļĄāļēāļĢāļāļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāđāļēāļ Data Preparation, Data Loading, Data Cleansing āļŦāļĢāļ·āļāļāļēāļĢāļāļģāļĢāļ°āļāļāļāļķāđāļ Production āļāļāļāđāļ§āļĨāļēāļāļģāļāļēāļāļāļāļāļīāļŦāļĢāļ·āļāđāļāļ§āļąāļāļŦāļĒāļļāļāđāļāđāđāļāđāļāļāļĢāļąāđāļāļāļĢāļēāļ§ āļāļēāļĄāļāļ§āļēāļĄāļāļģāđāļāđāļāļāļāļāđāļāļĢāļāļāļēāļĢ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Statistical Analysis, Problem Solving, Automation, TensorFlow, Leadership Skill
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
A good honors Degree, preferably at postgraduate level, in Computer Science, Software Engineering, Statistics, Mathematics or related disciplines. Extensive experience in Big 4 or MNC Consulting firm or industry related role. Machine learning and big data activities and demonstrate value through use cases. Applied experience in 2 or more types of machine learning problem classes -- forecasting, prediction, segmentation, optimization and cognitive use cases. Proficient at data quality assessment, data exploration, profiling, design and development of analytic data sets, statistical ...
āļāļąāļāļĐāļ°:
Digital Marketing, Statistics, Python
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor's Degree or higher in Computer Science, Computer Engineering, Data Science, Statistics, or any related field.
- Minimum of 2 years in AI/ML engineer, cloud solution or a related field.
- Proficiency in some of the following: Python, PySpark and SQL etc.
- Experience or strong interest in digital marketing, MarTech, and AdTech, especially data-driven marketing strategies is a plus.
- Experience in building tools / models to support retention, up-cross selling, optimization, mobile app data and digital marketing is a plus.
- Ability to communicate and collaborate with cross-functional teams.
- Growth mindset and openness to continuously learning and facing new projects and new technologies.
- Contact: K.Wannaporn 02------866.
- You have read and reviewed Krung Thai Bank Public Company Limited's Privacy Policy at https://krungthai.com/th/content/privacy-policy. The Bank does not intend or require the processing of any sensitive personal data, including information related to religion and/or blood type, which may appear on copy of your identification card. Therefore, please refrain from uploading any documents, including copy(ies) of your identification card, or providing sensitive personal data or any other information that is unrelated or unnecessary for the purpose of applying for a position on the website. Additionally, please ensure that you have removed any sensitive personal data (if any) from your resume and other documents before uploading them to the website.
- The Bank is required to collect your criminal record information to assess employment eligibility, verify qualifications, or evaluate suitability for certain positions. Your consent to the collection, use, or disclosure of your criminal record information is necessary for entering into an agreement and being considered for the aforementioned purposes. If you do not consent to the collection, use, or disclosure of your criminal record information, or if you later withdraw such consent, the Bank may be unable to proceed with the stated purposes, potentially resulting in the loss of your employment opportunity with.

āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļāļāļāļļāļ
AI āļāļāļāđāļĢāļēāļāļ°āļāđāļēāļāđāļŦāđ āđāļĨāđāļ§āļŦāļēāļāļēāļāļāļĩāđāđāļāđāļŠāļģāļŦāļĢāļąāļāļāļļāļ
āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
2 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Multitasking, Teamwork, Tableau, Python, Scala, Java, SQL, English, Thai
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- LINE MAN Wongnai is Thailand's Leading On-Demand Delivery and Lifestyle e-Commerce platform services. We build technology to help Thai people live better, to empower all local businesses by creating an end-to-end food ecosystem through our channel LINE MAN and Wongnai. Connected consumers, riders, and local businesses and improved the daily life of all parties with restaurants nationwide. And because we are local, we provide the deepest variety and services that are tailor-made for Thai people.
- We are looking for an experienced Data Scientist to oversee the research, working in ...
- Work closely with business teams to uncover insights and opportunities to effectively solve business problems.
- Gather requirements and build predictive machine learning models to guide decision making process.
- Design trustworthy AB testing experiments and evaluate the results.
- Work closely with the data engineering and product team on data collection and deploying models into production.
- Communicate actionable insights to stakeholders, senior management and decision makers through effective data visualization and presentation.
- Bachelor Degree in Computer Science, Statistics, Data Analytics, Business Administration, or related fields.
- 2+ years of experience in relevant fields.
- Strong SQL skills.
- Well versed in programming languages commonly used for data science such as Python, R. Knowledge of Java, Scala is a strong plus.
- Proven experience building models, deploying models into production, optimization and solving those problems at scale.
- Good understanding of distributed data systems, mathematics such as linear algebra, statistics and probability.
- Ability to use BI Tools such as Tableau, data studio.
- Have a growth mindset and willingness to learn new things and share knowledge with others.
- Great communication skills, organization skills, multitasking and teamwork.
- A good command of English and Thai.
- Experience in implementing search/recommendation systems.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Analytical Thinking, Data Analysis, TensorFlow, Big Data, Python, Hadoop
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- At Gosoft (Thailand) Co., Ltd., we are looking for an experienced.
- AI and Data Scientist.
- to lead the development of advanced AI and machine learning solutions that drive innovation and business impact across our digital platforms.
- In this role, you will design and deploy large-scale machine learning systems, develop advanced analytical models, and translate complex data into strategic insights. You will collaborate with cross-functional teams including engineering, product, and business stakeholders to solve high-impact challenges in the retail and e-commerce ecosystem.
- Our team builds data-driven solutions that support platforms such as.
- 7-Eleven Thailand.
- and.
- 7-Delivery., including Search Engines, Recommender Systems, Demand Forecasting, Customer Analytics, and other AI-powered retail solutions.
- Lead the.
- design, development, and deployment of advanced machine learning and AI solutions.
- to solve complex business problems.
- Develop and implement.
- predictive models, recommendation systems, forecasting models, and optimization algorithms.
- Architect and deploy.
- end-to-end machine learning pipelines., from data ingestion and feature engineering to model deployment and monitoring.
- Analyze large-scale structured and unstructured datasets to uncover.
- actionable insights and strategic opportunities.
- Collaborate with engineering, product, and business teams to translate.
- business challenges into scalable data science solutions.
- Drive the adoption of.
- best practices in machine learning, experimentation, and model governance.
- Provide.
- technical leadership and mentorship.
- to data scientists and data engineers.
- Evaluate and integrate.
- state-of-the-art AI/ML techniques.
- including deep learning, large language models, and advanced analytics.
- Work closely with data engineering teams to ensure.
- robust data pipelines and scalable ML infrastructure.
- Communicate insights and technical findings to both.
- technical and executive stakeholders.
- Required.
- Bachelors or Masters degree in.
- Computer Science, Data Science, Statistics, Mathematics, or a related field.
- 6-10+ years of experience.
- in data science, machine learning, or AI-related roles.
- Strong expertise in.
- machine learning, statistical modeling, and advanced analytics.
- Proficiency in.
- Python and data science ecosystems.
- (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, etc.).
- Experience designing.
- end-to-end machine learning pipelines and production ML systems.
- Strong experience working with.
- large-scale datasets and big data technologies.
- Ability to translate.
- complex data analysis into business insights and strategic recommendations.
- Strong problem-solving, analytical thinking, and leadership capabilities.
- Excellent communication skills and the ability to present technical concepts to.
- both technical and non-technical stakeholders.
- Preferred.
- Experience in.
- retail, e-commerce, or recommendation systems.
- Experience with.
- search ranking, recommender systems, personalization, or demand forecasting.
- Familiarity with.
- cloud platforms.
- such as AWS, Azure, or GCP.
- Experience with.
- big data technologies.
- such as Spark, Hadoop, or Databricks.
- Experience deploying models using.
- MLOps frameworks and tools.
- Knowledge of.
- deep learning, NLP, or generative AI.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
2 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Problem Solving, Data Analysis, Recruitment, Automation, Tableau, YouTube, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- At Agoda, we bridge the world through travel. Our story began in 2005, when two lifelong friends and entrepreneurs, driven by their passion for travel, launched Agoda to make it easier for everyone to explore the world.
- Today, we are part of Booking Holdings [NASDAQ: BKNG], with a diverse team of over 7,000 people from 90 countries, working together in offices around the globe. Every day, we connect people to destinations and experiences, with our great deals across our millions of hotels and holiday properties, flights, and experiences worldwide.
- No two days are the same at Agoda. Data and technology are at the heart of our culture, fueling our curiosity and innovation. If you're ready to begin your best journey and help build travel for the world, join us.
- THIS ROLE IS BASED IN BANGKOK, THAILAND (WITH RELOCATION PROVIDED).
- Location: Bangkok, Thailand (*Not open for remote work).
- The Supply Analytics team delivers data-driven insights and decision support to optimize Agoda's supply ecosystem. We analyze partner and marketplace dynamics, build forecasts and models, run experiments, and create scalable dashboards and data products that power strategic direction and operational execution. We bring value through rigorous analyses and pragmatic, measurable recommendations that help Supply, Product and other teams prioritize work and drive impact. Our work improves inventory quality, partner economics, and the long-term health and growth of Agoda.
- As a Senior Analyst, you will report directly to either the Senior Manager or Associate Director within the Supply Department and this will be an individual contributor role. You will be responsible and fully empowered to work with the partners on the ground. You will be supported by a team within the Supply department and work closely with other Team members within Agoda.
- In this Role, you'll get to.
- Translate internal briefs into analytical projects (to include refining the initial brief and asking the 'right questions', working through potential hypotheses and storyboarding the output).
- Use and analyze data from multiple large-scale data warehouses and present statistically strong analysis to a wide range of business stakeholders.
- Proactively identify opportunities for growth within supply and the wider business.
- Drive new analytical initiatives and projects aimed at improving organizational efficiency and shaping Agoda supply.
- Identify, support, and lead projects aimed at scaling up the way the Supply organization leverages on data, insights, and intelligence.
- Automate manual operational processes and present back on time savings gained through modernization of business operations.
- What you'll Need to Succeed.
- At least 2-5+ years of experience working as an Analyst with experience in analytics/data science/insights/strategy/BI.
- Advanced working knowledge and hands-on experience in SQL.
- Strong knowledge and hands-on experience in data visualization tools such as Tableau (preferably).
- Expert domain of data analysis and data visualization tools and software such as Excel, Python (or R).
- Bachelor's degree ideally in a business or quantitative subject (e.g. computer science, mathematics, engineering, science, economics or finance).
- A good understanding of statistical modelling knowledge or any machine learning technique knowledge (such as hypothesis testing, regression, logistic regression, random forest, etc.).
- Good stakeholder management experience. Comfortable presenting to senior leadership and C-suite.
- Experience in conducting A/B testing experimentation.
- Strong experience in finding data insights and provide business recommendation to the business.
- A hacker's mindset - the ability to build simple but clever and elegant solutions to new problems within significant resource, operational and time constraints through deep understanding of the business, creative problem solving, and a wide range of expertise in data, analytics, automation, programming, and prototyping.
- Excellent communicator with superior written, verbal, presentation and interpersonal communication skills.
- Data driven in both decision making and performance measurement.
- Extreme comfort in ambiguous, fast-paced environment.
- Ability to multi-task, prioritize and coordinate resources.
- MBA or Masters in a quantitative subject (e.g. computer science, mathematics, engineering, science, economics or finance).
- Program management certifications (e.g. PMI, PRINCE2) to compliment your program management experience.
- Asian market experience.
- Travel industry / e-commerce / tech / consulting experience.
- sanfrancisco.
- Please review our Hiring Process Guidelines before your interview click.
- here.
- to learn how interviewing at Agoda works.
- Discover more about working at Agoda.
- Agoda Careers.
- https://careersatagoda.com.
- Facebook.
- https://www.facebook.com/agodacareers/.
- LinkedIn.
- https://www.linkedin.com/company/agoda.
- YouTube.
- https://www.youtube.com/agodalife.
- Equal Opportunity Employer.
- At Agoda, we pride ourselves on being a company represented by people of all different backgrounds and orientations. We prioritize attracting diverse talent and cultivating an inclusive environment that encourages collaboration and innovation. Employment at Agoda is based solely on a person's merit and qualifications. We are committed to providing equal employment opportunity regardless of sex, age, race, color, national origin, religion, marital status, pregnancy, sexual orientation, gender identity, disability, citizenship, veteran or military status, and other legally protected characteristics.
- We will keep your application on file so that we can consider you for future vacancies and you can always ask to have your details removed from the file. For more details please read our privacy policy.
- Disclaimer.
- We do not accept any terms or conditions, nor do we recognize any agency's representation of a candidate, from unsolicited third-party or agency submissions. If we receive unsolicited or speculative CVs, we reserve the right to contact and hire the candidate directly without any obligation to pay a recruitment fee.
āļāļąāļāļĐāļ°:
Power BI, Tableau, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Collaborate with stakeholders to determine data collection needs and appropriate data resources for specific projects or initiatives.
- Responsible for development, deployment and sustainment of algorithm and data model.
- Evaluate and recommend other database & analytics technologies.
- Participate in special projects and performs other duties as assigned.
- Experience in SQL, Python/R and data visualization (e.g. Tableau/PowerBI).
- Perform advanced analysis (hypothesis testing, predictive model, clustering, etc).
- Excellent communication skills (verbal and written).
āļāļąāļāļĐāļ°:
Statistical Analysis
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
- Analyze data from company databases to drive optimization and improvement of product development, marketing techniques, and business strategies.
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
- Develop company A/B testing framework and test model quality.
- Coordinate with different functional teams to implement.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Multitasking, Architecture, Teamwork, Python, SQL, English, Thai
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- LINE MAN Wongnai is Thailand's Leading On-Demand Delivery and Lifestyle e-Commerce platform services. We build technology to help Thai people live better, to empower all local businesses by creating an end-to-end food ecosystem through our channel LINE MAN and Wongnai. Connected consumers, riders, and local businesses and improved the daily life of all parties with restaurants nationwide. And because we are local, we provide the deepest variety and services that are tailor-made for Thai people.
- We are looking for an experienced Data Scientist to oversee the research, working in ...
- Improve Recommendation Services.
- Building deep knowledge around LINE MAN Wongnai's business related to recommendation services.
- Building on, developing, and enhancing the architecture of the interconnected network of predictive and analytic data models.
- Building scalable prototypes of data tools for testing and proof-of-concept.
- Data and Analytics.
- Analyzing results of experiments with rigorous statistical methods to conclude and suggest strategies to improve the business of LMWN.
- Performing data enrichment and profiling through inferential statistics based on multi-dimensional customer and merchant behavioural data.
- Identifying growth opportunities by analyzing LINE MAN Wongnai's vast amounts of data, develop models that can be used for optimization to ensure platform's growth.
- Technology.
- Researching and building new technological to enhance recommendation capabilities for LMWN.
- Knowledge in natural language processing, information retrieval domain, or knowledge graph is a plus.
- Bachelor Degree in Computer Science, Statistics, Data Analytics, or related fields.
- 3-7 years of experience in data science, statistical or mathematical research, operations research, or complex data modeling and analytics.
- Evidence of previous projects (personal or work-related) with complex analytics and statistical/machine learning techniques using R/Python.
- Strong SQL skills.
- Good understanding of statistics, computer science, and data, and the ability to implement this understanding in complex business situations.
- A hacker's mindset - the ability to build simple but clever and elegant solutions to new problems within significant resource, operational and time constraints.
- Familiarity with the mathematics underpinning analytics (linear programming, linear algebra, convex optimisations, etc.) is a plus.
- Great communication skills, organization skills, multitasking and teamwork.
- A good command of English and Thai.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Multitasking, Accounting, Teamwork, Python, SQL, English, Thai
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- LINE MAN Wongnai is Thailand's Leading On-Demand Delivery and Lifestyle e-Commerce platform services. We build technology to help Thai people live better. We empower all local businesses by creating an end-to-end ecosystem through our channel LINE MAN and Wongnai to connect and improve the daily life of consumers, riders, and local businesses nationwide. And because we are local, we provide the deepest variety and services that are tailor-made for Thai people.
- We are looking for an experienced Data Scientist to lead the science behind our Fina ...
- Credit Risk Modeling & Measurement.
- Develop, implement, and maintain core credit risk models, including application scorecards, behavioral models, and PD / LGD / EAD estimation frameworks.
- Leverage diverse data sources transactional, behavioral, financial, sequential and alternative unstructured data for feature engineering and model innovation.
- Own ECL measurement and monitoring, ensuring models accurately reflect portfolio risk across products, cohorts, and vintages.
- Design robust model evaluation, back-testing, and recalibration processes to maintain performance under changing borrower behavior and market conditions.
- Quantify and manage trade-offs between disbursement growth, risk appetite, and profitability.
- Experimentation.
- Design and analyze controlled experiments or policy tests (e.g., limit changes, approval threshold adjustments) to measure causal impact on risk and profitability.
- Ensure changes to credit policies are supported by rigorous evidence and clearly understood risk implications.
- Cross-Functional Collaboration.
- Partner closely with the business team, product, engineering, accounting and operations to align modeling outputs with operational constraints and business objectives.
- Support model governance, documentation, and audit requirements, including alignment with internal risk frameworks and regulatory expectations.
- Bachelor Degree in Computer Science, Statistics, Quantitative Finance, Economics, or related fields.
- 5+ years of experience in data science, credit risk, or quantitative modeling; experience in BNPL, consumer lending, or fintech is strongly preferred.
- Strong understanding of PD, ECL, and portfolio risk concepts, with the ability to visualize and translate them into practical business decisions.
- Strong Python and SQL skills.
- Great communication skills, organization skills, multitasking and teamwork.
- Good command of English and Thai.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Architecture, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Be the Authority on Causal Truth: You will apply quasi-experimental methods and structural modeling to safeguard the business from mistaking correlation for causation. You will ensure our biggest bets are backed by rigorous evidence rather than coincidence.
- Solve Big, Ambiguous Questions.
- You will help build the research agenda for the "unknowns" of our marketplace. You will investigate foundational topics like long-run market dynamics, system efficiency ...
- Simplify Strategy.
- You will distill immense complexity into clear, applicable mental models. You will partner with senior leadership to cut through the noise, helping them define the true goals and principles of the business.
- What Essential Skills You Will Need.
- Industry Experience.
- At least 5 years of experience with marketplace design, pricing strategy or economic consulting.
- Technical Skills.
- Econometrics: Expertise in causal inference techniques like difference-in-differences and regression discontinuity.
- Structural thinking: ability to build models predicting market equilibrium and effects of pricing changes.
- Technical proficiency: SQL and statistical programming (R, Python) is necessary.
- Strategic Communication: ability to simplify complex economic concepts to partners and influence decisions.
- Experience with economic logic, structural thinking, and causal analysis, regardless of their tenure in industry.
- A Master's/PhD degree in Economics, or a related field is preferred.
- Additional Information.
- Life at Grab.
- We have your back with.
- Term Life Insurance.
- and comprehensive Medical Insurance.
- With GrabFlex, create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave.
- We have a confidential.
- Grabber Assistance Programme.
- to guide and uplift you and your loved ones through life's challenges.
- Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours.
- What We Stand For at Grab.
- We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
āļāļąāļāļĐāļ°:
Good Communication Skills, Power BI, Tableau, Python, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
Get to Know the Team Grab - the leading super app in Southeast Asia - combines transport, food delivery, logistics, payments, and much more in a single platform. The Country & Marketing Analytics team helps the organisation by analysing in each of our countries our past operations and marketing initiatives, deciphering the impact on our business, our brand and our users, to augment the outcome of future initiatives. Get to Know the Role You will be working with various teams in Grab - Country Business, Operations, Marketing as well as Engineering, Product and other Data team to und ...

āļāļĢāļ°āđāļĄāļīāļāđāļāļīāļāđāļāļ·āļāļ
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āļĨāļāļāļāļģ 5 āļŠāļīāđāļāļāļĩāđāļŦāļĨāļąāļāđāļĨāļīāļāļāļēāļ āļāļĩāļ§āļīāļāļāļļāļāļāļ°āđāļāļĨāļĩāđāļĒāļāđāļāļāļĨāļāļāļāļēāļĨ
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