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āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļāļāļāļļāļ
AI āļāļāļāđāļĢāļēāļāļ°āļāđāļēāļāđāļŦāđ āđāļĨāđāļ§āļŦāļēāļāļēāļāļāļĩāđāđāļāđāļŠāļģāļŦāļĢāļąāļāļāļļāļ
āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļąāļāļĐāļ°:
Video Editing, Videography
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ15,000 - āļŋ23,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļŠāļēāļĄāļēāļĢāļāļāđāļēāļĒāļ§āļĩāļāļĩāđāļāđāļāđāļāļĩ āļāļēāļĄāļāļĩāđāđāļāđāļĢāļąāļāđāļāļāļĒāđ āļāđāļēāļĒāđāļāđāļāļāļļāļāđāļāļ āļāđāļēāļĒāļ āļēāļāļāļīāđāļāļŦāļĢāļ·āļāļ āļēāļāđāļāļ·āđāļāļāļŦāļĨāļąāļāđāļāđ.
- āļāļąāļāļāđāļāļ§āļĩāļāļĩāđāļāđāļŦāđāđāļāđāļāļēāļĄāđāļāļāļĒāđāļāļĩāđāđāļāđāļĢāļąāļ.
- āļŠāļēāļĄāļēāļĢāļāļāļāļāļēāļāļāļąāļāļāđāļāļāđāļēāļāđāđāļāđāļāđāļ§āļĒāļāļąāļ§āđāļāļ āļāļąāđāļāđāļĢāļ·āđāļāļāļ āļēāļ/āđāļŠāļĩāļĒāļ āđāļĨāļ°āļāļĢāļēāļāļāļīāļ.
- āļŠāļēāļĄāļēāļĢāļāļāļąāļāļāļāļāđāļāļĢāļ°āļāļāļāđāļŠāļāđāļāđ.
- āļāļĢāļ°āļāļąāļāļŠāļąāļāļāļĄ.
- āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩ.
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāļĢāļ°āļāļģāļāļĩ.
- āļāđāļēāļāļģāļāļēāļāļĨāđāļ§āļāđāļ§āļĨāļē.
- āđāļāļāļąāļŠāļāļēāļĄāļāļĨāļāļēāļāđāļĨāļ°āļāļĨāļāļĢāļ°āļāļāļāļāļēāļĢ.
- āļāļāļāļąāļāļāļēāđāļĨāļ°āļŠāļļāļāļ āļēāļ 20,000 āļāļēāļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3 āļāļĩ).
- āļāļĢāļ°āļāļąāļāļŠāļļāļāļ āļēāļāđāļĨāļ°āļāļļāļāļąāļāļīāđāļŦāļāļļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3 āļāļĩ āļŦāļĢāļ·āļāđāļāđāļ Senior āļāļķāđāļāđāļ).
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāđāļēāļāļāļĢāļ°āđāļāļĻ āļāļĢāļāļĩāļĒāļāļāļāļēāļĒ āļāļĢāļ°āļāļģāļāļĩāļāļĩāđāļāļģāļŦāļāļāļāļķāļāđāļāđāļē āļāđāļēāļāđāļāļĢāļāļĢāļķāđāļāļāļĩāđāļĢāļ.
- āļāļāļĢāļ§āļĄāļāļāļąāļāļāļēāļ āļāļāļĄāđāļĨāļ°āļāđāļģāļŠāđāļ§āļāļāļĨāļēāļ 5,000 āļāļēāļ āļāđāļāđāļāļ·āļāļ.
- Incentive āļĢāļēāļĒāđāļāļĢāļĄāļēāļāļĢ āđāļĄāļ·āđāļāļāļķāļāđāļāđāļēāļāļĩāđāļāļģāļŦāļāļ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Oracle, ERP
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Lead the integration architecture between Oracle Retail applications, Oracle Fusion Cloud, and other source systems using Oracle GoldenGate-based integration, APIs, file-based interfaces, middleware, and database-level integration where applicable.
- Ensure that business transactions from Oracle Retail and other applications are accurately captured, transformed, validated, and recorded in Oracle Fusion for financial and operational processing.
- Lead and develop a team of ERP and integration application developers responsible fo ...
- Collaborate with business stakeholders from Finance, Retail Operations, Procurement, Supply Chain, IT, and other departments to gather requirements and translate them into technical solutions, integrations, reports, and support processes.
- Work closely with Oracle Fusion functional teams, Oracle Retail teams, database administrators, infrastructure teams, and external implementation/support partners to deliver stable and scalable solutions.
- Ensure effective integration between Oracle SaaS applications, Oracle Retail applications, on-premise systems, cloud platforms, data platforms, and third-party applications.
- Define and enforce integration standards, data validation rules, error handling mechanisms, reconciliation controls, retry processes, and audit trails for transaction recording into Oracle Fusion.
- Provide technical leadership for incident resolution, root cause analysis, service requests, production issues, and performance problems related to Oracle Fusion, Oracle Retail integrations, GoldenGate replication, and connected applications.
- Ensure compliance with enterprise IT standards, security policies, data governance requirements, audit controls, and regulatory expectations.
- Identify opportunities to improve integration reliability, automation, monitoring, performance, scalability, and maintainability.
- Support project execution, system testing, cutover planning, migration activities, production deployment, hypercare, and post-go-live stabilization.
- Ensure proper documentation of technical designs, integration flows, data mappings, operational procedures, support runbooks, and knowledge transfer materials.
- Build internal capability so that the Makro / CP AXTRA application team can enhance, maintain, monitor, and support the Oracle Fusion integration landscape after project handover.
- Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
- Minimum of 5 years of experience in ERP implementation, integration, or production support for enterprise ERP platforms such as Oracle Fusion Cloud, Oracle E-Business Suite, SAP, or similar systems.
- Strong experience with Oracle Fusion Cloud applications, especially in finance-related modules such as General Ledger, Payables, Receivables, Fixed Assets, Procurement, or related ERP processes.
- Experience with Oracle Retail applications, retail transaction systems, merchandising systems, inventory systems, or store/back-office integration is highly desirable.
- Hands-on experience or strong working knowledge of Oracle GoldenGate, data replication, CDC-based integration, transaction synchronization, and reconciliation processes.
- Experience integrating Oracle Fusion with upstream and downstream systems using APIs, FBDI, BI Publisher reports, Oracle Integration Cloud, database procedures, flat files, middleware, or other integration technologies.
- Familiarity with Oracle Autonomous Database, Oracle Database, PL/SQL, Oracle APEX, Oracle Integration Cloud, Oracle Data Integrator, Microsoft SQL Server, Java,.NET, shell scripting, or Python is preferred.
- Strong understanding of finance transaction recording, accounting interfaces, period-end processing, reconciliation, error handling, and audit requirements.
- Knowledge of ERP and retail business processes, including procurement, inventory, sales, supplier invoices, payments, financial accounting, and management reporting.
- Demonstrated leadership experience in managing technical teams, vendors, and cross-functional project stakeholders.
- Experience in retail, wholesale, FMCG, or large enterprise environments is advantageous.
- Strong analytical and problem-solving skills, with the ability to investigate complex integration, data, and application issues.
- Good understanding of IT controls, application security, data governance, change management, and production support practices.
- Effective communication skills, both verbal and written, with the ability to explain technical topics clearly to business users, management, vendors, and technical teams.
- Preferred Experience.
- Oracle Fusion Cloud ERP implementation or support experience.
- Oracle Retail integration experience.
- Oracle GoldenGate implementation or operational support experience.
- Experience supporting high-volume transaction interfaces from retail systems into ERP.
- Experience with transaction monitoring, automated reconciliation, exception handling, and batch/interface support.
- Experience working with finance, procurement, supply chain, and retail operations teams.
- Experience managing system cutover, migration, production deployment, and hypercare activities.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
6 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Cloud Computing, Architecture, Postgre SQL, Automation, Leadership Skill
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- AI & Advanced Analytics Enablement.
- Lead the design, development, and deployment of enterprise AI, Machine Learning, and Generative AI solutions to support business transformation (e.g., pricing, promotion, automation, recommendation, demand forecasting, customer insights, and intelligent decision support).
- Build and manage scalable data pipelines supporting AI/ML model training, inference, feature engineering, Feature Store, RAG knowledge bases, and LLM applications.
- Develop reusable datasets, AI-ready data products, feature engineering pipelines, and semantic data models for AI Engineers, Data Scientists, and Business Analytics teams.
- Partner closely with AI Engineers to design, deploy, productionize, and scale AI/ML models, LLM applications, Agentic AI, AI Chatbots, Recommendation Systems, and Mobile AI applications.
- Design and maintain data ingestion pipelines for structured, semi-structured, and unstructured data from enterprise systems, APIs, databases, files, event streams, IoT devices, and third-party platforms.
- Develop scalable data pipelines supporting document ingestion, embedding generation, metadata management, vector indexing, and retrieval workflows for RAG applications.
- Identify opportunities to embed AI into business workflows and operational decision-making to improve efficiency, customer experience, and business value.
- Data Platform & Engineering Leadership.
- Own end-to-end enterprise data architecture from source systems to Data Lake, Lakehouse, Data Warehouse, Feature Store, Semantic Layer, and AI-serving layers.
- Design, develop, and optimize scalable ETL/ELT pipelines supporting batch, micro-batch, streaming, and near real-time data processing.
- Design and maintain workflow orchestration for enterprise data pipelines using Databricks Workflows, Apache Airflow, or equivalent orchestration frameworks.
- Develop enterprise-scale Big Data solutions using Apache Spark and distributed computing frameworks.
- Design scalable logical and physical data models while optimizing database architecture for performance, scalability, reliability, and cost efficiency.
- Ensure enterprise data quality, governance, lineage, metadata management, observability, security, and compliance across data platforms.
- Implement automated data validation, monitoring, logging, alerting, and observability to ensure production-grade data reliability.
- Optimize SQL queries, Spark workloads, partitioning strategies, storage formats, and compute resources for maximum performance and cost efficiency.
- Analyze complex technical issues, identify root causes, troubleshoot production problems, and recommend infrastructure and platform improvements.
- Select, evaluate, and integrate modern data engineering tools, cloud technologies, and AI platform frameworks to support evolving business needs.
- Continuously evaluate emerging technologies in Big Data, Lakehouse Architecture, Data Platform Engineering, Cloud Computing, and AI Platform Engineering.
- AI Platform & Infrastructure.
- Build and maintain enterprise AI data infrastructure supporting LLM, RAG, Agentic AI, AI Chatbots, Recommendation Engines, Intelligent Search, and Intelligent Automation platforms.
- Design and implement scalable AI data pipelines supporting batch, streaming, vector search, embedding pipelines, and Retrieval-Augmented Generation (RAG) architectures.
- Design scalable APIs, data services, and integration layers connecting AI applications with enterprise systems and digital platforms.
- Collaborate with AI Engineers to prepare high-quality datasets for LLM fine-tuning, prompt engineering, model evaluation, inference, and continuous model improvement.
- Support deployment and operationalization of AI products using DataOps, MLOps, CI/CD, containerization, and modern software engineering practices.
- Drive continuous improvements in platform scalability, availability, security, observability, resilience, and operational efficiency.
- Team Leadership & Capability Building.
- Lead, mentor, and develop a high-performing team of Data Engineers, AI Engineers, and Analytics professionals.
- Foster engineering excellence through best practices in architecture design, coding standards, testing, code reviews, deployment, technical documentation, and software engineering.
- Drive architecture reviews, technical design reviews, engineering governance, and platform standardization across the engineering organization.
- Define engineering standards, technical roadmaps, platform architecture, and technology strategy aligned with business objectives.
- Collaborate closely with Product Owners, Business Stakeholders, AI Engineers, Data Scientists, Solution Architects, Infrastructure, and DevOps teams.
- Provide hands-on technical leadership with a strong engineering mindset and willingness to troubleshoot complex production systems.
- Promote continuous learning, knowledge sharing, innovation, and adoption of emerging technologies across the engineering team.
- Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, Data Engineering, Management Information Systems, or a related field.
- 6+ years of experience in Data Platform Engineering, Data Engineering, Big Data, or AI Platform development, with experience leading engineering teams.
- Strong experience designing and implementing enterprise-scale Data Lake, Lakehouse, Data Warehouse, or Modern Data Platform architectures.
- Expert proficiency in SQL, Databricks SQL, PostgreSQL, database design, and query performance optimization.
- Strong programming skills in Python.
- Hands-on experience with Apache Spark, Databricks, Delta Lake, Spark SQL, Unity Catalog, and distributed data processing.
- Strong experience designing and developing scalable ETL/ELT pipelines and workflow orchestration.
- Experience with streaming technologies such as Spark Structured Streaming, Kafka, or equivalent.
- Experience building enterprise-scale data platforms supporting AI/ML, Advanced Analytics, and Generative AI applications.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
5 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Market Analysis, Negotiation
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Formulate sourcing strategies aligned with Commercial Buyers to achieve category goals.
- Create SKU selection roadmaps using market insights and consumer preferences.
- Scout innovative sourcing origins and product concepts to diversify inventory.
- Market & Competitor Analysis.
- Benchmark market movements and competitor tactics using industry research platforms.
- Leverage consumer data to uncover sourcing gaps and growth prospects.
- Commercial Alignment & Cost Management.
- Gather and align on sourcing requirements from Commercial Teams.
- Manage COGS calculations, MOQ verification, and lead-time coordination.
- Negotiate volumes strategically to optimize costs and inventory levels.
- Product Development & Launch Readiness.
- Compile documentation and samples for the New Product Introduction Committee.
- Ensure offerings match market positioning and company objectives.
- Coordinate with QA, RA, SCM, Design, and Legal on compliance and quality.
- Performance Monitoring & Campaign Planning.
- Track sales, margin, and inventory metrics for sourced products.
- Partner with Commercial and Marketing teams on promotional initiatives.
- Required.
- Bachelor's degree in Business, International Trade, Supply Chain, or similar.
- 5-7 years in e-commerce buying or global sourcing for FMCG/retail sectors.
- Fluent in English (both written and spoken).
- Proficiency with market research tools (Euromonitor, Nielsen).
- Strong negotiation and analytical capabilities.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
1 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Project Management, Power BI, Tableau, English, Thai
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Oversee cross-functional process enhancement initiatives in supply chain and logistics.
- Execute Lean and Six Sigma programs targeting service, productivity, and cost efficiency.
- Create standardized procedures and best practices across the supply chain network.
- Establish and manage the Supply Chain Center of Excellence.
- Build KPI frameworks and performance dashboards.
- Manage project management governance for WMS/TMS deployments and network optimization.
- Collaborate with internal teams and external logistics partners.
- Lead organizational change management and skill-development initiatives.
- Required.
- Bachelor's degree in Supply Chain, Engineering, or Business (MBA preferred).
- 10-15 years in Supply Chain, Logistics, Process Improvement, or Transformation roles.
- Demonstrated expertise in retail, FMCG, wholesale, or 3PL sectors.
- Track record with large-scale transformation programs.
- Lean/Six Sigma certification preferred.
- Proficiency in Excel, Power BI, Tableau, ERP, WMS, or TMS systems.
- Strong stakeholder management capabilities.
- Fluency in both Thai and English.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Product Development, Postgre SQL, Automation, Javascript, Kubernetes
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, develop, and fine-tune Generative AI solutions using leading LLMs such as GPT-4o, Claude (Anthropic), Gemini, and open-source models (LLaMA, Mistral, etc.).
- Build and maintain End-to-End AI Chatbot systems - from prompt engineering and LLM integration to deployment and monitoring.
- Develop production-grade RAG (Retrieval-Augmented Generation) Chatbot pipelines integrating vector search, document chunking, embedding models, and LLM inference.
- Implement and manage Vector Storage solutions using FAISS, Qdrant, Weaviate, Pinecone, or similar technologies for semantic search and knowledge retrieval.
- Design and deploy Agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom multi-agent orchestration workflows.
- Integrate AI capabilities with enterprise APIs, databases, and internal platforms via LangChain, LlamaIndex, or equivalent tooling.
- Stay current with rapidly evolving AI/ML tools including Hugging Face, Ollama, vLLM, OpenAI APIs, Vertex AI, AWS Bedrock, and Azure OpenAI Service.
- AI Product Development & MLOps.
- Own the end-to-end AI product lifecycle: requirements gathering, model selection, development, testing, deployment, and continuous improvement.
- Build and manage scalable AI inference pipelines with performance monitoring, logging, and cost optimization.
- Design prompt engineering strategies, evaluation frameworks, and guardrails for safe and reliable LLM outputs.
- Implement CI/CD workflows for AI models using MLflow, DVC, or similar MLOps tools.
- Collaborate with Data Science and Engineering teams to productionize machine learning and AI models.
- Identify opportunities to embed AI capabilities into business workflows, automation, and decision-making processes.
- Data Engineering & Platform (Foundational Awareness).
- Understand the fundamentals of data pipeline concepts (ETL/ELT, batch and streaming) to effectively collaborate with Data Engineering teams.
- Able to read, query, and work with data from common platforms such as Databricks, Snowflake, BigQuery, or PostgreSQL.
- Understand data flow from source systems to data warehouses/lakehouses to inform AI model inputs and outputs.
- Familiar with vector database concepts and embedding pipelines sufficient to configure and use them in AI projects.
- Capable of communicating data requirements clearly to Data Engineers when building AI features or knowledge bases.
- Collaboration & Innovation.
- Partner with product managers, business stakeholders, and engineers to translate business requirements into AI solutions.
- Contribute to and promote best practices for AI development, testing, documentation, and responsible AI usage.
- Mentor junior engineers and share knowledge on emerging AI tools, frameworks, and research.
- Evaluate and adopt new AI/ML technologies and frameworks to keep the team at the forefront of innovation.
- Effectively manage multiple priorities and deliver high-quality solutions independently and as part of a team.
- Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, or a related field.
- 3+ years of hands-on experience in AI/ML engineering with a focus on Generative AI and NLP applications.
- Proven experience building Generative AI solutions using GPT-4o, Claude (Anthropic), Gemini, or equivalent LLMs - this is a must.
- Hands-on experience with RAG Chatbot development: embedding pipelines, chunking strategies, retrieval tuning, and LLM response generation.
- Proficiency in Vector Storage technologies such as FAISS, Qdrant, Weaviate, Pinecone, or ChromaDB.
- Experience building End-to-End Chatbot systems including intent handling, context management, multi-turn dialogue, and API integration.
- Practical knowledge of Agentic AI frameworks (LangGraph, AutoGen, CrewAI, or similar) for building multi-step, tool-using AI agents.
- Strong Python programming skills - Python (required), SQL, JavaScript/TypeScript (a plus).
- Familiarity with popular AI/ML tools and platforms: LangChain, LlamaIndex, Hugging Face, Ollama, vLLM, OpenAI SDK, Vertex AI, AWS Bedrock, or Azure OpenAI.
- Experience with prompt engineering techniques including few-shot prompting, chain-of-thought, structured output, and function calling.
- Ability to evaluate LLM outputs using metrics and frameworks (RAGAS, TruLens, or custom eval pipelines).
- AI Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen, CrewAI, Hugging Face Transformers.
- LLM Platforms: OpenAI (GPT-4o), Anthropic Claude, Google Gemini, Meta LLaMA, Mistral.
- Vector Databases: FAISS, Qdrant, Weaviate, Pinecone, ChromaDB.
- Cloud & Data: AWS / GCP / Azure AI services, Databricks, Snowflake, PostgreSQL.
- MLOps: MLflow, DVC, Docker, Kubernetes (a plus).
- Big Data: Apache Spark, Kafka (a plus).
- Experience in Retail or E-Commerce AI applications (e.g., recommendation engines, pricing AI, conversational commerce).
- Familiarity with fine-tuning and PEFT techniques (LoRA, QLoRA) for LLMs.
- Knowledge in machine/statistical learning, computer vision, or time series forecasting.
- Experience with AI safety, hallucination mitigation, and responsible AI practices.
- Contributions to open-source AI projects or published AI research.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļąāļāļĐāļ°:
Content Creator
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ15,000 - āļŋ20,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļĨāļīāļāļāļāļāđāļāļāļāđāļāļĩāđāļāđāļēāļŠāļāđāļāđāļāļ·āđāļāļĨāļāļŠāļ·āđāļāļāļāļāđāļĨāļāđāļĨāļāđāļāļĨāļāļāļāļĢāđāļĄāļāđāļēāļ āđ āđāļāđāļ Facebook, Line OA.
- āļāļđāļŠāļīāļāļāđāļēāļāļĩāđāļāđāļēāļŠāļāđāļāđāļĨāļ°āļāļīāļāđāļāļĢāļāļāđāļāđāļēāļāļķāļāļāļđāļāļāļēāļĄāļĢāļđāļāđāļāļāļāļĢāļīāļĐāļąāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļ āļ§āļēāļāđāļāļ āļāļąāļāļāļēāļĢāđāļāļĄāđāđāļĨāļāđāđāļāļāļēāļĢāļĨāļāļāļāļāđāļāļāļāđāļĨāđāļ§āļāļŦāļāđāļēāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄāļāļēāļĢāļāļĨāļēāļ.
- āļāļđāđāļĨāļ āļēāļāļĢāļ§āļĄāļāļāļāļāļāļāđāļāļāļāđāđāļŦāđāđāļāļāđāļāđāļ āļāļāđāļāļāļĨāļąāļāļĐāļāđāļĨāļ°āļāļąāļ§āļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āđāļĨāļ°āļāļ·āđāļ āđ āļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āļĄāļĩāļāļąāļĻāļāļāļāļīāđāļāļāļēāļĢāļāļģāļāļēāļāđāļāļīāļāļāļ§āļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļāļŠāļđāļ.
- āļŠāļēāļĄāļēāļĢāļāļāļāļāđāļāđāļĢāļāļāļāļāļēāļāļāļēāļĢāļāļģāļāļēāļāļāļąāļāđāļāđāļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļāļīāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđ āļāļąāļāļŠāļĄāļąāļĒ.
- āļĄāļĩāļĄāļāļļāļĐāļĒāđāļŠāļąāļĄāļāļąāļāļāđāļāļĩāđāļāļĩāļŠāļēāļĄāļēāļĢāļāļāļīāļāļāđāļāļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļāļđāđāļĢāđāļ§āļĄāļāļēāļāđāļĨāļ°āļāļāļąāļāļāļēāļāđāļāđāļāļāļāļāļ·āđāļāđ āđāļāđāđāļāđāļāļāļĒāđāļēāļāļāļĩ.
- āļāļĢāļ°āļāļąāļāļŠāļąāļāļāļĄ.
- āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩ.
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāļĢāļ°āļāļģāļāļĩ.
- āļāđāļēāļāļģāļāļēāļāļĨāđāļ§āļāđāļ§āļĨāļē.
- āđāļāļāļąāļŠāļāļēāļĄāļāļĨāļāļēāļ.
- āļāļāļāļąāļāļāļēāđāļĨāļ°āļŠāļļāļāļ āļēāļ 20,000 āļāļēāļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3āļāļĩ).
- āļāļĢāļ°āļāļąāļāļŠāļļāļāļ āļēāļāđāļĨāļ°āļāļļāļāļąāļāļīāđāļŦāļāļļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3 āļāļĩ āļŦāļĢāļ·āļāđāļāđāļ Senior āļāļķāđāļāđāļ).
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāđāļēāļāļāļĢāļ°āđāļāļĻ āļāļĢāļāļĩāļĒāļāļāļāļēāļĒ āļāļĢāļ°āļāļģāļāļĩāļāļĩāđāļāļģāļŦāļāļāļāļķāļāđāļāđāļē āļāđāļēāļāđāļāļĢāļāļĢāļķāđāļāļāļĩāđāļĢāļ.
- āļāļāļĢāļ§āļĄāļāļāļąāļāļāļēāļ āļāļāļĄāđāļĨāļ°āļāđāļģāļŠāđāļ§āļāļāļĨāļēāļ 5,000 āļāļēāļ āļāđāļāđāļāļ·āļāļ.
- āļāļ·āđāļāđāļāļēāļĄāļāđāļāļāļāļĨāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āđāļ§āļĨāļēāļāļģāļāļēāļ.
- āđāļ§āļĨāļēāļāļēāļ āļāļąāļāļāļĢāđ-āļĻāļļāļāļĢāđ 9.00-18.00 āļ.
- āļŦāļĄāļđāđāļāđāļēāļ āđāļāļĢāļāļāļēāļĢ āļāļąāļĒāļĒāđ āļĢāļēāļāļāļĪāļāļĐāđ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ15,000 - āļŋ16,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ, āļĄāļĩāļāđāļēāļāļāļĄāļĄāļīāļāļāļąāđāļ
- āļ§āļēāļāđāļāļāđāļĨāļ°āļāļģāđāļāļīāļāļāļēāļĢāļāļĨāļĒāļļāļāļāđāļāļēāļĢāļāļēāļĒāđāļĨāļ°āļāļēāļĢāļāļĨāļēāļāđāļāļ·āđāļāđāļāļīāđāļĄāļĒāļāļāļāļēāļĒ.
- āļŠāļĢāđāļēāļāđāļĨāļ°āļĢāļąāļāļĐāļēāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļĩāđāļāļĩāļāļąāļāļĨāļđāļāļāđāļēāđāļŦāļĄāđāđāļĨāļ°āļĨāļđāļāļāđāļēāđāļāļīāļĄ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĨāļēāļāđāļĨāļ°āļāļđāđāđāļāđāļāđāļāļ·āđāļāļāļĢāļąāļāļāļĢāļļāļāļāļĨāļĒāļļāļāļāđāļāļēāļĢāļāļēāļĒ.
- āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāļāļēāļĢāļāļēāļĒāđāļĨāļ°āļāļēāļĢāļāļĨāļēāļāđāļāļ·āđāļāļāļĢāļ°āđāļĄāļīāļāļāļĨ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļāļĩāļĄāļāļēāļāļ āļēāļĒāđāļāđāļāļ·āđāļāđāļŦāđāļāļĢāļīāļāļēāļĢāļĨāļđāļāļāđāļēāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩāđāļāļŠāļēāļāļēāļāļēāļĢāļāļĨāļēāļ, āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļļāļĢāļāļīāļ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāđāļēāļāļāļēāļĢāļāļēāļĒāļŦāļĢāļ·āļāļāļēāļĢāļāļĨāļēāļāļāļĒāđāļēāļāļāđāļāļĒ 2 āļāļĩ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢāđāļĨāļ°āļāļēāļĢāđāļāļĢāļāļēāļāđāļāļĢāļāļāļāļĩāđāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāļĢāđāļāļĢāļĄ Microsoft Office āđāļāđāļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļāļīāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđāđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļģāļāļēāļāļ āļēāļĒāđāļāđāļāļ§āļēāļĄāļāļāļāļąāļāđāļāđ.
- āļāļģāđāļĄāļāđāļāļāļŠāļĄāļąāļāļĢāļāļēāļāļāļĩāđ?.
- āļĢāđāļ§āļĄāļāļēāļāļāļąāļāļāļĢāļīāļĐāļąāļāļāļĩāđāļĄāļĩāļāļ§āļēāļĄāļĄāļąāđāļāļāļāđāļĨāļ°āđāļāļīāļāđāļ āļāļĢāđāļāļĄāđāļāļāļēāļŠāđāļāļāļēāļĢāļāļąāļāļāļēāļāļąāļāļĐāļ°āđāļĨāļ°āļāļ§āļēāļĄāļāđāļēāļ§āļŦāļāđāļēāđāļāļŠāļēāļĒāļāļēāļāļĩāļ āļāļĢāđāļāļĄāļŠāļ§āļąāļŠāļāļīāļāļēāļĢāļāļĩāđāļāđāļēāļŠāļāđāļāđāļĨāļ°āļāļĩāļĄāļāļēāļāļāļĩāđāđāļāđāļāļĄāļīāļāļĢ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Leadership Skill, Power BI, Python, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Examining operational metrics and performance data across the delivery network.
- Creating visual tracking systems and analytical reports for stakeholder review.
- Spotting patterns and efficiency gaps to recommend improvements.
- Delivering data-driven guidance to senior leadership.
- Measuring productivity, service standards, delivery speed, and expenditures.
- Assisting with demand forecasting and workforce planning.
- Collaborating with Operations teams on performance enhancements.
- Leading process optimization projects.
- Generating periodic performance summaries for executive presentations.
- Required.
- Bachelor's degree in Business, Logistics, Supply Chain, Engineering, Statistics, or related discipline.
- Minimum 3 years in Operations, Logistics, Business Analytics, or Performance Management roles.
- Advanced proficiency in Microsoft Excel.
- Demonstrated expertise with Power BI, SQL, Python, or comparable analytics platforms.
- Capacity to convert sophisticated datasets into actionable business conclusions.
- Strong analytical and problem-solving capabilities.
- KPI development and management experience.
- Excellent communication and cross-departmental collaboration abilities.
- Proficiency handling competing demands in dynamic work settings.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
8 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Product Development, Market Research, Data Analysis, Negotiation, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Conducts market research and analysis to identify products that can be developed and adopted as Makro's Commercial Non-Food Own Brand.
- Feeds back market information, competition information, projects, sales and profit results to line manager and branded team.
- Work with branded team to conduct assortment review and identify Own Brand NPD plan.
- Coordinates with concerned Associate Director, Senior Buyer and Buyers to identify existing shelf-products that are sellable and can generate desired sales and profits if developed and marketed as Makro's Commercial Non-Food Own Brand. Identifies suppliers who can co-develop these products.
- Launch new OB items on time and to targeted cost, to achieve Commercial Non-Food Own Brand new launch budget SKUs and sales.
- Cooperates and maintains good relationship with suppliers who are reliable in providing OB products. Monitors the performance of Commercial Non-Food Own Brand products, identify root causes of shortfall and plan actions to improve performance.
- Check and ensure branded buyer maintains information/ data bank for OB products by encoding in the computerized system all correct articles and suppliers' data.
- Ensures that products being developed match or better quality than branded benchmarks and priced lower than the benchmark according to brand positioning.
- Ensures that product quality and efficacy meet Makro and Thai FDA standards and customers' expectations, if necessary, to obtain certificate.
- Work with branded buyer to produce an effective Makro Mail Program, to achieve sales budget and profit targets. Gives all necessary information and documents (i.e. article, supplier, ordering, promotions) to the stores.
- Conducts market research and analysis to identify products that can be developed and adopted as Makro's Commercial Non-Food Own Brand.
- Feeds back market information, competition information, projects, sales and profit results to line manager and branded team.
- Coordinates with concerned Director, Sr. Buyer and Buyers to identify existing shelf-products that are sellable and can generate desired sales and profits if developed and marketed as Makro's Own Brand. Identifies suppliers who can co-develop these products.
- Launch new OB items on time and to targeted cost, to achieve Own Brand new launch budget SKUs and sales.
- Cooperates and maintains good relationship with suppliers who are reliable in providing OB products. Monitors the performance of Own Brand products, identify route causes of shortfall and plan actions to improve performance.
- Check and ensure branded buyer maintains information/ data bank for OB products by encoding in the computerized system all correct articles and suppliers data.
- Ensures that products being developed match or better quality than branded benchmarks and priced lower than the benchmark according to brand positioning.
- Work with branded buyer to produce an effective Makro Mail Program, to achieve sales budget and profit targets. Gives all necessary information and documents (i.e. article, supplier, ordering, promotions) to the stores.
- Makes regular store visits to detect operational problems and follows-up on product quality and presentation. Gives all the necessary information to the Commercial Director and concerned Sr Buyer and Buyers to which the own brand is categorized/ grouped.
- Travel, when necessary (i.e. to visit suppliers and producers).
- Commits to all agreements made with the suppliers (i.e. invoice control arrangements, price differences), all of which are subject to a written contract or confirmation.
- Makes regular store visits to detect operational problems and follows-up on product quality and presentation. Gives all the necessary information to the Commercial Director and concerned Sr Buyer and Buyers to which the own brand is categorized/ grouped.
- Travel, when necessary (i.e. to visit suppliers and producers).
- Commits to all agreements made with the suppliers (i.e. invoice control arrangements, price differences), all of which are subject to a written contract or confirmation.
- Performs any other tasks not specified herewith as required / instructed by superior / management.
- Bachelor's degree or higher in Business Administration, Marketing, Supply Chain, Industrial Distribution, or a related field.
- Minimum 5-8 years of experience in merchandising, category management, buying, or own-brand/private label development within retail, FMCG, or wholesale businesses.
- Proven experience in Own Brand / Private Label product development, including NPD planning, supplier collaboration, and product launch execution.
- Strong understanding of category management, assortment planning, pricing strategy, and sales & margin optimization.
- Demonstrated ability to analyze market trends, competitor benchmarks, and customer insights to identify commercially viable product opportunities.
- Experience working closely with suppliers, manufacturers, and internal stakeholders (Commercial, QA, Marketing, Sales Support).
- Solid knowledge of product quality standards, Thai FDA regulations, and certification processes; experience coordinating with QA teams is an advantage.
- Strong commercial acumen with the ability to manage cost structures, target pricing, and profitability objectives.
- Proven track record of managing multiple projects simultaneously and delivering product launches on time and within budget.
- Excellent analytical, decision-making, and problem-solving skills with a results-oriented mindset.
- Strong communication, negotiation, and influencing skills, capable of building productive relationships across all levels.
- High level of attention to detail, organizational skills, and data accuracy in system management.
- Ability to work independently and collaboratively in a fast-paced, cross-functional environment.
- Proficient in MS Office and merchandising or ERP systems; data analysis skills are a plus.
- Good command both of spoken and written English.

āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļāļāļāļļāļ
AI āļāļāļāđāļĢāļēāļāļ°āļāđāļēāļāđāļŦāđ āđāļĨāđāļ§āļŦāļēāļāļēāļāļāļĩāđāđāļāđāļŠāļģāļŦāļĢāļąāļāļāļļāļ
āļāļąāļāđāļŦāļĨāļāđāļĢāļāļđāđāļĄāđāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
1 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Video Editing, Videography, Photography, Photo Editing
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ15,000 - āļŋ22,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļąāļāļāđāļāļ§āļīāļāļĩāđāļāļāđāļ§āļĒāđāļāļĢāđāļāļĢāļĄ Premiere Pro, After Effects, Photoshop āļŦāļĢāļ·āļāđāļāļĢāđāļāļĢāļĄāļāļ·āđāļāđ āđāļāđ.
- āļāļāļāđāļāļāļāļēāļ āļāļąāļāļāđāļāļāļĨāļīāļ āđāļŠāđāđāļāđāļāđāļāļ āļāļĢāļąāļāđāļāđāļāļŠāļĩ āđāđāļĨāļ°āđāļŠāļĩāļĒāļ Sound Effect āđāļŦāđāđāļŦāļĄāļēāļ°āļāļąāļāđāļāļ·āđāļāļŦāļē.
- āļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđāļāļĨāļāļēāļ āļŠāļēāļĄāļēāļĢāļāļāđāļēāļĒāđāļĨāļ°āļāļąāļāļāđāļāļ§āļīāļāļĩāđāļ āļāļģāđāļāļĨāļāļāļēāļĄāđāļāļĨāļāļāļāļĢāđāļĄāļāđāļēāļ āđ.
- āļŠāļēāļĄāļēāļĢāļāļāļģ infographic, motion graphic āļŦāļĢāļ·āļāļāļāļāđāļāļĢāļ°āļāļāļāļāđāļēāļāđ āļŠāļģāļŦāļĢāļąāļāļāļĢāļ°āļāļāļāļāļĨāļīāļāļ§āļīāļāļĩāđāļ.
- āļ§āļēāļāđāļāļāļāļēāļĢāļāļąāļāļāđāļāđāļŦāđāđāļŠāļĢāđāļāļāļēāļĄāļāļģāļŦāļāļ āđāļĨāļ°āļāļĢāđāļāļĄāļāļĢāļąāļāđāļāļĨāļĩāđāļĒāļāđāļāđāđāļāļāļēāļĄ feedback āđāļāđāļāļĒāđāļēāļāļĢāļ§āļāđāļĢāđāļ§.
- āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄ Production āđāļĨāļ° Marketing āđāļāļ·āđāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđāļāļīāđāļāļāļēāļāļ§āļīāļāļĩāđāļāļāļĩāđāļĄāļĩāļāļļāļāļ āļēāļ.
- āļŠāļēāļĄāļēāļĢāļāļŦāļē Footage āļĢāļđāļāļ āļēāļ āđāļĨāļ°āļ§āļīāļāļĩāđāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāļāļēāļĢāđāļĨāđāļēāđāļĢāļ·āđāļāļ (Story Telling) āđāļāđāļāļĩ.
- āļŠāļēāļĄāļēāļĢāļāļāļąāļāļāļēāļĢāļāļēāļāđāļŦāđāđāļŠāļĢāđāļāļāļēāļĄāđāļ§āļĨāļēāļāļĩāđāļāļģāļŦāļāļāđāļāđ.
- āđāļĨāļ°āļāļ·āđāļ āđ āļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āļāļģāļāļēāļāļāđāļēāļāļāļēāļĢāđāļāđāļāļĨāđāļāļ Mirrorless āļāļąāđāļāļ āļēāļāļāļīāđāļāđāļĨāļ°āļ§āļĩāļāļĩāđāļ.
- āļāļģāļāļēāļāļāļēāļĢāđāļāđāđāļāļĢāđāļĢāļāļĄ Adobe Pr, Ae, Ai, Ps, Lr.
- āļŠāļēāļĄāļēāļĢāļāļāļąāļāļāļāļāđāļāļĢāļ°āļāļāļāļ āļēāļāđāļāđāļāļĩ āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāđāļĢāļ·āđāļāļāļāļāļāļāļēāļĢāļ§āļēāļ Layout āļ āļēāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāļāļģāļāļēāļĢāļāļĨāļēāļāļāļāļāđāļĨāļāđ.
- āđāļāđāļāļāļđāđāļāļĩāđāļŠāļēāļĄāļēāļĢāļāļāļģāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļĨāļ°āđāļĨāđāļēāđāļĢāļ·āđāļāļāđāļāđāļāļĩ āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāđāļēāļĒāļāļģāļ§āļīāļāļīāđāļ.
- āļĄāļĩāļāļ§āļēāļĄāļāļīāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđ āļāļģāļāļēāļāļ āļēāļĒāđāļāđāđāļĢāļāļāļāļāļąāļāđāļāđāļāļĩ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļŠāđāļāļāļēāļāđāļāđāļāļēāļĄāļāļģāļŦāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļāļŠāļđāļ āļāļĢāļāļāđāļāđāļ§āļĨāļē āļĄāļĩāļāļāļĢāđāļāļāļĨāļāļēāļāđāļāļ·āđāļāļĄāļēāļāļģāđāļŠāļāļāđāļāđāļēāļāļąāđāļ.
- āļāļĢāļ°āļāļąāļāļŠāļąāļāļāļĄ.
- āļ§āļąāļāļŦāļĒāļļāļāļāļĢāļ°āļāļģāļāļĩ.
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāļĢāļ°āļāļģāļāļĩ.
- āļāđāļēāļāļģāļāļēāļāļĨāđāļ§āļāđāļ§āļĨāļē.
- āđāļāļāļąāļŠāļāļēāļĄāļāļĨāļāļēāļāđāļĨāļ°āļāļĨāļāļĢāļ°āļāļāļāļāļēāļĢ.
- āļāļāļāļąāļāļāļēāđāļĨāļ°āļŠāļļāļāļ āļēāļ 20,000 āļāļēāļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3 āļāļĩ).
- āļāļĢāļ°āļāļąāļāļŠāļļāļāļ āļēāļāđāļĨāļ°āļāļļāļāļąāļāļīāđāļŦāļāļļ (āđāļĄāļ·āđāļāļāļģāļāļēāļāļāļĢāļ 3 āļāļĩ āļŦāļĢāļ·āļāđāļāđāļ Senior āļāļķāđāļāđāļ).
- āļāļĢāļīāļāļāđāļāļāđāļāļĩāđāļĒāļ§āļāđāļēāļāļāļĢāļ°āđāļāļĻ āļāļĢāļāļĩāļĒāļāļāļāļēāļĒ āļāļĢāļ°āļāļģāļāļĩāļāļĩāđāļāļģāļŦāļāļāļāļķāļāđāļāđāļē āļāđāļēāļāđāļāļĢāļāļĢāļķāđāļāļāļĩāđāļĢāļ.
- āļāļāļĢāļ§āļĄāļāļāļąāļāļāļēāļ āļāļāļĄāđāļĨāļ°āļāđāļģāļŠāđāļ§āļāļāļĨāļēāļ 5,000 āļāļēāļ āļāđāļāđāļāļ·āļāļ.
- Incentive āļĢāļēāļĒāđāļāļĢāļĄāļēāļāļĢ āđāļĄāļ·āđāļāļāļķāļāđāļāđāļēāļāļĩāđāļāļģāļŦāļāļ.

āļāļĢāļ°āđāļĄāļīāļāđāļāļīāļāđāļāļ·āļāļ
āđāļāđāļāļ§āđāļēāļāļļāļāļāļ§āļĢāđāļāđāđāļāļīāļāđāļāļ·āļāļāđāļāđāļēāđāļŦāļĢāđ āļāļēāļāļāđāļāļĄāļđāļĨāļāļĨāļēāļāđāļĨāļ°āļāļąāļāļĐāļ°āļāļāļāļāļļāļ
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