Data Scientist

• Own the end-to-end modelling and estimation behind promotion optimisation — elasticity, heterogeneous treatment effects, budget-constrained allocation, and incrementality — from problem framing to production.
• Advance our causal inference stack: experiment and quasi-experiment design (geo/switchback tests, holdouts, diff-in-diff, synthetic control), debiasing observational data, and variance reduction — raising the bar on experimentation rigor across the team.
• Design and productionize heterogeneous treatment effect (uplift) models at scale (tens of millions of users), with honest offline evaluation (uplift/Qini curves, policy-value estimation).
• Formulate and solve budget-constrained allocation under fairness and dynamic business constraints — from LP/MILP to greedy or Lagrangian methods where they scale better.
• Mentor and technically guide a team of data scientists; set standards for model evaluation, documentation, and scientific review.
• Partner with business to turn model outputs into budget decisions, and communicate tradeoffs (subsidy efficiency vs. growth) clearly.

Principal Data Scientist – RPL

• Participate in and conduct research on decision-quality analysis and continuous improvement of the project
• Develop and research statistical algorithms and machine learning models; use SQL and Python to validate and build production applications of large language models (LLMs)
• Extract and analyze data to validate hypotheses and deliver quantifiable, actionable conclusions with clear implementation directions
• Assist in producing analytical reports and technical documentation; collaborate with the team to drive project progress

Data Scientist, AI Agent Quality

• Translate complex business challenges into well-defined technical problems solvable with data, statistics, and machine learning.
• Collaborate with product managers, engineers, and business stakeholders to build and scale data science solutions that power pricing across ride-hailing, food delivery and logistics
• Own the full ML lifecycle—from ideation and research to model development, pipeline implementation, deployment, experimentation, and driving measurable business outcomes.
• Improve the efficiency of our dynamic pricing system using techniques from econometrics, causal inference and simulation
• Design and interpret experiments to measure model impact, working with analysts and product teams to ensure rigorous evaluation and clear success metrics.
• Monitor and evaluate model performance, identifying areas for improvement and proposing solutions
• Communicate insights, trade-offs, and technical decisions effectively to cross-functional stakeholders, and operate with a high degree of autonomy in ambiguous problem spaces.

Senior Manager, Data Scientist – Pricing

• Provide data-driven insights, dashboards, and scenario modeling to support demand and supply planners, S&OE/S&OP processes, and enterprise decision-making
• Drive continuous improvement in analytics, data quality, and model reliability
• Drive automation and advanced analytics, leveraging Python, SQL, and BI tools to scale insights
• Partner with more senior team members to align analytics with standards, governance, and process maturity efforts
• Generate dashboards and KPIs that support IBP governance and forum discussions
• Enable S&OE/S&OP with near-term supply-demand coverage analytics, including early warning indicators

Data Scientist, Integrated Business Planning

• Partner with stakeholders to clarify business questions into ML problem statements (classification, ranking, uplift, forecasting, optimization, GenAI RAG/agentic workflows, etc.).
• Write and maintain an ML System Design Spec: problem hypothesis, decision loop, users, constraints, acceptable risk, SLAs/SLOs, and post-deployment guardrails.
• Conduct advanced exploratory data analysis on large datasets using Python, pyspark, SQL, and visualization libraries.
• Design, implement, and validate machine learning and statistical models to address complex healthcare and insurance challenges.
• Collaborate with DevOps engineers to productionize models using containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
• Build and maintain reusable ML accelerators that standardize feature engineering, model training, and evaluation across tasks.
• Facilitate technical workshops and presentations to ensure clarity and buy-in across diverse audiences.
• Advocate for responsible AI by incorporating fairness, explainability, and bias detection into model development.

Data Scientist jobs in Singapore

See all jobs on JobTailor

Principal Data Scientist – RPL

Data Scientist, AI Agent Quality

Senior Manager, Data Scientist – Pricing

Data Scientist, Integrated Business Planning

Senior Data Scientist