Principal Data Scientist - Rewards Promo Loyalty, Rpl

What You Will Do

Own the end-to-end modeling and estimation behind promotion optimization — elasticity, heterogeneous treatment effects, budget-constrained allocation, and increamentality — 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.

What You Will Need

8+ years in data science or ML, with 3+ years focused on causal inference or uplift modeling in production settings.
Deep expertise in heterogeneous treatment effect estimation, with hands-on production experience in several of: meta-learners (S/T/X/R), causal forests, DR-learner, or deep uplift architectures.
Hands-on experience optimizing promotions, pricing, or marketing incentives with evolving constraints and measurable business outcomes.
Strong grounding in experimentation and observational causal methods — propensity weighting, instrumental variables, synthetic control, difference-in-differences.
Experience with constrained optimization (MILP, Lagrangian methods, etc.) applied to resource allocation.
Proficiency in Python and SQL; shipping models to production with engineering partners.
Track record of technical leadership at principal/staff level: setting technical direction for a team, reviewing high-stakes analyses, and influencing roadmaps and partner teams 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 for sequential incentive decisions
• Publications or open-source contributions in causal ML (e.g., work building on EconML, CausalML, or the uplift literature)
• Experience operating across multiple markets/geographies in Southeast Asia

About the Team

You'll join the Budget Efficiency Data Science team, a group of data scientists focused on making Gojek's promotions and incentives spend as effective as possible across our core services. We combine machine learning, causal inference, and optimization to make sure our budget drives real, measurable impact for the business and our customers. We value rigor, collaboration, and turning complex problems into decisions that leaders can act on. Our mission is to make every rupiah of investment count — and we're excited for you to be part of it.