Data Analytics Engineer

Day to Day Responsibilities

1. Data engineering & analytics
o    Collect, clean, validate and pre-process structured and unstructured data (text, image, video, and audio) from plant, operational and business sources.
o    Build and maintain scalable data models, datasets, ETL/ELT pipelines and data-quality checks;  ensure timely, accurate and traceable data availability.
o    Conduct statistical analysis and develop visualizations, dashboards and recurring reports to identify operational improvement opportunities in CHOCC plant.
o    Translate business questions into analytical approaches and clearly communicate findings and recommended actions to stakeholders.

2. Machine learning & advanced analytics
o    Develop, train, evaluate and deploy fit-for-purpose machine-learning models to identify patterns, predict outcomes and support plant performance improvement.
o    Optimize model accuracy, robustness and performance; monitor model results and iterate based on business feedback.

3. AI & intelligent agents / AI 与智能体
o    Design, build, test and improve AI-powered agents for data analysis, automated reporting and decision support.
o    Apply LLMs for data understanding, code generation, summarization and insight generation, including prompt design, evaluation and iteration.
o    Integrate AI agents with approved data sources and workflows (e.g., Python scripts, APIs and databases), while assessing limitations, risks, privacy and compliance requirements.

4. Project Leadership, Collaboration & Documentation 
o    Lead end-to-end data analytics, automation and AI-enabled projects, from opportunity identification and requirements gathering through solution design, development, deployment and benefit tracking.
o    Partner with plant teams, Digital/IT and other cross-functional stakeholders to define project scope, prioritize use cases, align resources and deliver practical, scalable solutions.
o    Drive project plans, milestones, risks, stakeholder communications and issue resolution to ensure timely delivery and realization of targeted business benefits and cost reductions.
o    Establish and maintain clear documentation for data definitions, pipelines, analytical methodologies, model/agent logic, technical designs and project outcomes.
o    Promote knowledge sharing, user adoption and continuous improvement of data, analytics and AI solutions across the plants.

Education & Experience

•    Bachelor’s degree or above in Computer Science, Data Science, Statistics, Industrial Engineering, Automation or a related discipline.
•    5 years’ Relevant experience in data analytics, data engineering, business intelligence or digital/manufacturing analytics;  strong candidates with relevant project experience may be considered. 
•    Experience working with manufacturing, operational or supply-chain data is preferred.

Required Skills

•    Proficiency in Python and SQL;  hands-on experience with data analysis libraries and tools such as Pandas, NumPy, Excel and Power BI.
•    Solid understanding of data cleaning, data modeling, ETL/ELT, statistical analysis, visualization and data-quality management.
•    Working knowledge of machine-learning concepts, common algorithms, model evaluation and performance tuning.
•    Foundational understanding of generative AI, LLMs and AI-agent concepts (e.g., tool use, workflow orchestration and prompt-based reasoning), and the ability to apply them responsibly to analytics, coding assistance or reporting.
•    Awareness of data privacy, cybersecurity and responsible-AI principles.
•    Strong problem-solving, stakeholder communication and documentation skills; able to work effectively across functions.
•    Professional working proficiency in English and Chinese.

Desired Skills

•    Experience with cloud data/AI platforms (e.g., AWS), APIs, version control (Git) and databases/data warehouses.
•    Familiarity with data orchestration, MLOps, dashboard development or deployment of analytics solutions.
•    Experience with manufacturing systems/data (e.g., MES, ERP, SCADA/historian) and Lean/continuous-improvement practices.
•    Experience evaluating LLM applications or building AI-agent workflows is a plus.