Senior Data Scientist (Advanced Insights)

Position Summary

We are looking for a Senior Data Scientist (Advanced Insights) to be based in our Kuala Lumpur office. The role's responsible include transforming operational data into actionable insights that improve business performance. The role will require to own the end-to-end delivery: problem framing, dataset and metric design, dashboard development, and the implementation of GenAI-enabled solutions that sit on top of governed enterprise data. The ideal candidate should be delivery-oriented, collaborative, and capable of shipping reliable solutions that are adopted by business users.

Key Responsibilities

Business & Decision Analytics

• Partner with stakeholders to identify high-impact opportunities where analytics and GenAI improve

decision-making and process performance.

• Translate business needs into analytical questions, KPI definitions, reporting requirements, and GenAI

user stories.

• Manage a portfolio of deliverables with clear success measures and measurable outcomes.

Analytics Engineering & Metric Governance

• Extract, clean, transform, and integrate data from multiple systems to produce trusted analytics

datasets.

• Design and maintain analytics data models (e.g., star schema / subject-area models) to support

reporting and retrieval.

• Define auditable metric logic and ensure consistency, data quality, and traceability of key measures.

Dashboards, Reporting & Data Storytelling

• Design and develop operational dashboards and reports that clearly communicate performance, trends,

and exceptions.

• Present insights in business language with clear implications and recommended actions.

• Iterate with users to improve usability, clarity, and adoption.

GenAI Solutions (RAG, Chat, Structured Extraction)

• Design and implement GenAI workflows including RAG pipelines (ingestion, chunking, embeddings,

retrieval, prompting).

• Build document Q&A and structured extraction solutions (documents → fields/JSON) with validation

and post-processing.

• Build lightweight API services (e.g., FastAPI/Flask) to enable integration with internal systems and

workflows.

Mentoring & Standards

• Mentor junior analysts / team members on problem framing, SQL, data modelling, dashboard best

practices, and GenAI evaluation hygiene.

• Define and promote templates and standards for KPI definitions, dashboards, documentation, and

evaluation.

Required Qualifications

• 6–8+ years of experience in data analytics, BI, data science, data engineering, or adjacent roles with

end-to-end delivery ownership.

• Strong quantitative reasoning and ability to translate ambiguous business problems into structured

analyses and decisions.

• Demonstrated proficiency in:

o SQL and relational databases (e.g., PostgreSQL or similar)

o Python for data manipulation and analysis

o Dashboarding and data storytelling fundamentals (tool-agnostic)

• Practical experience delivering at least one of the following in a production or near-production setting:

o Retrieval-Augmented Generation (RAG) using a vector database

o LLM-based structured extraction into JSON / schemas

o LLM integration via APIs into an end-user workflow

• Strong communication and stakeholder management skills; able to deliver iteratively in time-boxed

environments.

Advantages / Preferred Qualifications

• Education: Degree in Computer Science, Mathematics, Statistics, Engineering, or other STEM

disciplines is an advantage.

• Cloud: Experience with AWS services (including data/analytics services and/or AWS Bedrock) is an

advantage.

• Qualifications: Relevant AWS certifications (e.g., cloud

practitioner/associate/professional/specialty) are an advantage.

• Experience with vector stores (FAISS, Qdrant, Milvus, Pinecone) and retrieval tuning.

• Experience with document formats and parsing (PDF/XML/HTML) and robust postprocessing/

validation approaches.

• Familiarity with Docker, CI/CD, and basic observability/monitoring practices.

• Exposure to security/privacy practices (PII handling, access controls).

• Domain exposure in operations contexts (maintenance, supply chain, manufacturing,

plantation/process industries).