Principal Data Scientist - Rewards, Promo & Loyalty

About the role

Gojek is looking for a Principal Data Scientist to lead the science behind its voucher and incentive programs across food delivery, ride-hailing and logistics. Promotional spend reaches millions of consumers weekly, and every rupiah is allocated by models you will help design. This is a hands-on technical leadership role within the Budget Efficiency Data Science team, which combines machine learning, causal inference and optimization to make promotions and incentives spend as effective as possible.

Key responsibilities

• Own end-to-end modeling and estimation for promotion optimization: elasticity, heterogeneous treatment effects, budget-constrained allocation and incrementality, from problem framing to production.
• Advance the causal inference stack: experiment and quasi-experiment design (geo/switchback tests, holdouts, diff-in-diff, synthetic control), debiasing observational data and variance reduction.
• Design and productionize heterogeneous treatment effect (uplift) models at tens of millions of users, with honest offline evaluation (uplift/Qini curves, policy-value estimation).
• Solve budget-constrained allocation under fairness and dynamic business constraints, using LP/MILP or greedy/Lagrangian methods where they scale better.
• Mentor and guide a team of data scientists, set standards for model evaluation, documentation and scientific review, and turn model outputs into budget decisions with clear trade-offs.

Required profile

• 8+ years in data science or ML, with 3+ years focused on causal inference or uplift modeling in production.
• Deep expertise in heterogeneous treatment effect estimation, with production experience in meta-learners, causal forests, DR-learner or deep uplift architectures.
• Hands-on experience optimizing promotions, pricing or marketing incentives with measurable business outcomes.
• Technical leadership at principal/staff level: setting technical direction, reviewing high-stakes analyses and influencing roadmaps without direct authority.
• Experience in marketplaces, ride-hailing, food delivery, e-commerce or fintech.
• Nice to have: familiarity with off-policy evaluation, bandits or reinforcement learning; publications or open-source contributions in causal ML; experience across Southeast Asian markets.

Required skills

• Propensity weighting, instrumental variables, synthetic control, difference-in-differences
• Constrained optimization (MILP, Lagrangian methods)
• Python and SQL, shipping models to production with engineering partners