Data Science & GenAI Lead: Build Predictive Engines

Machine Learning & Data Analytics (Build the Business Engine)

• Design, build, and validate predictive models end to end - for example churn prediction, Customer Lifetime Value (LTV), and demand or behaviour forecasting - from feature scoping through to deployment.
• Establish A/B testing and statistical validation practices suited to a single-owner function today, documented so they can be handed off cleanly as the team grows.
• Partner directly with data engineering (or the equivalent function/vendor) to define the business logic, feature requirements, and data pipelines needed for model training.
• Own the translation of model outputs into clear, actionable recommendations for non-technical department heads and stakeholders.
• Track and report the business KPIs tied to deployed models (e.g., revenue lift, cost avoidance, retention improvement) - not just delivery of the model itself.

Generative AI Strategy & LLM Evaluation (Build the Innovation Edge)

• Define and prototype architecture for initial LLM/GenAI use cases (e.g., internal copilots, retrieval-augmented generation tools), designed so they can extend into multi-agent systems later.
• Set baseline evaluation protocols for LLM accuracy, response quality, and hallucination risk, and ensure PDPA/PII compliance before any GenAI tool goes into production use.
• Use AI-assisted code-generation tools to rapidly prototype and benchmark proof-of-concept GenAI solutions, and make evidence-based calls on what to scale or kill.
• Make and document build-vs-buy and vendor decisions (LLM providers, vector databases, cloud AI services) within an agreed budget envelope.
• Act as the go-to internal expert on RAG optimisation, vector database selection, and prompt engineering.

Foundation-Building & Scaling Readiness (The Leadership Runway)

• Act as the direct technical liaison to the CTO and relevant department heads, translating R&D progress and architectural trade-offs into a roadmap leadership can fund and act on.
• Establish documentation, coding, and model-governance standards now so the function is ready to onboard a team later.
• Build the business case and role specifications for the first hires into the function, in partnership with the CTO.
• Own responsible-AI basics for the department - PDPA/PII compliance, basic bias checks, and an incident-response approach for model or AI failures.
• Drive adoption of predictive and GenAI outputs among business users, so work delivered is actually used, not just shipped.

Job Requirements

• Degree in Data Science, Computer Science, Statistics, Engineering, or a related field (or equivalent practical experience).
• Technical Skills:
• Python, SQL, and statistical/predictive modelling; A/B testing methodology. AI-first / GenAI engineering - agentic coding and AI-assisted development tools (e.g. Claude Code), LLM application architecture, RAG optimisation, vector database selection, and prompt engineering. Working familiarity with MLOps basics (model deployment, monitoring, versioning) sufficient to run production models without a dedicated platform team.
• Domain Knowledge:
• Strong understanding of retail/FMCG business drivers (customer behaviour, churn, demand patterns) and data-privacy/compliance considerations relevant to AI systems (PDPA/PII), enabling the role to bridge technical and business needs.
• Clear technical documentation; ability to translate complex model/architecture trade-offs for non-technical leadership; comfortable operating and advising senior stakeholders as a sole contributor without a supporting team; strong problem-solving.
• Demonstrated experience designing, building, and deploying predictive models in a production business environment, plus hands‑on experience building GenAI/LLM applications (RAG pipelines, vector databases, prompt engineering) beyond proof‑of‑concept.