Senior Data Scientist

Position Summary

We are seeking a Senior Data Scientist (Advanced Insights) to join our Kuala Lumpur office. This role is responsible for transforming operational data into actionable insights to enhance business performance. The successful candidate will manage end-to-end delivery, including problem framing, dataset and metric design, dashboard development, and the implementation of GenAI-enabled solutions built on governed enterprise data. The ideal candidate is delivery-oriented, collaborative, and skilled at delivering 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 can 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 data 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, highlighting clear implications and recommended actions.

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

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 (converting documents to fields/JSON) with validation and post-processing.

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

Mentoring & Standards

• Mentor junior analysts and 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 related 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:

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

• Python for data manipulation and analysis

• Dashboarding and data storytelling fundamentals (tool-agnostic)

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

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

• LLM-based structured extraction into JSON/schemas

• 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 post-processing/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).