Data Analyst / BI

Capcon; Kuala Lumpur; On-site; MYR 70,000 - 100,000; Full time

Job summary

A Fortune 500 engineering company in Kuala Lumpur is seeking a data-savvy professional with a background in Data Science or Business Intelligence. The role focuses on predictive analytics, data engineering, and driving supply chain efficiency. Ideal candidates will have 3+ years of experience, proficiency in Python or SQL, and strong analytical skills. Join a dynamic team with opportunities for career growth, contributing to digital transformation initiatives in a collaborative environment.

Qualifications

• 3 years in Data Science, Business Intelligence, or quantitative modelling.
• Proficient in Python or R or strong SQL skills.
• Commercial acumen and storytelling with data required.

Responsibilities

• Develop predictive models for demand forecasting and inventory optimization.
• Create simulation tools for strategic planning and scenario analysis.
• Manage and transform datasets from ERP systems and build automated data pipelines.

Skills

Data Science

Business Intelligence

Supply Chain Analytics

Python

SQL

Education

Degree in Data Science, Maths, Computer Science, or Actuarial

Tools

Power BI

SAP

Cloud platforms (e.g., Snowflake, Azure, AWS)

Job description

• We are recruiting for a EU engineering company > Fortune 500 company with 110,000 staff
• Job security, great benefits, HUGE (and clean) data sets (in SAP)

Projects

• Drive continuous improvement across the Asia Pacific Regional Supply Chain for Agricultural Solutions by leveraging data analytics to identify inefficiencies and implement data‑driven solutions.
• This role also leads digitalization and automation efforts, including data extraction, dashboard development in Power BI, and predictive analytics enablement.

This role would be perfect for someone with a business intelligence or dash‑boarding / Power BI background who wants to build their career in data science or someone in a Data Science team who now wants most responsibility.

Responsibilities

• Analytics & Modelling: Develop predictive models for demand forecasting, inventory optimization & lead time prediction.
• Decision Support & Scenario Planning: Create simulation tools to support strategic planning and what‑if analysis. Monitor and refine models to ensure optimal performance.
• Data Engineering & Automation: Manage and transform large datasets from ERP (e.g., SAP). Bonus > Build automated data pipelines using Python, SQL, or similar tools.

Career growth after 1 year > What you can learn!

• Apply machine learning techniques (anomaly detection, planning data analysis) to solve supply chain challenges.
• Digital Supply Chain Projects: Lead / co‑lead digital transformation initiatives (control towers, predictive maintenance, AI in logistics). Collaborate with IT and data teams to enhance infrastructure & modelling capabilities.

Team Culture

• New team of 2 (1 senior manager from Germany, 1 manager from Malaysia) the team does not come from a pure data science background (More BI). So this would be the first hire in a new initiative (lots of quick wins).
• Work together to present reports, insights and dashboards can help senior leadership make data driven decisions.

Must Have

• Commercial acumen, ask the right questions, communicate answers and tell the story behind the data.
• 3 years in Data Science, Business Intelligence, Supply Chain Analytics or quantitative modelling.
• Proficient in Python or R or strong SQL skills.

Nice to have

• Cloud platforms (e.g., Snowflake, Azure, AWS) and machine learning libraries (e.g., scikit‑learn, TensorFlow).
• Familiarity with Supply Chain Planning and Execution Systems (e.g., SAP IBP, Kinaxis).

Levels:

• Emphasis on hiring for potential (you do not need to be an expert at all of the above) > as long as you know the fundamentals / theory + some experience we would love to consider you.
• 3-4 years experience after graduation (DataScience, Maths, Computer Science, Actuarial)
• This could be in a big (or small) company or from consulting (any industry i.e Pharma, Retail, FMCG, banking)