Principal Data Scientist

We’re looking for a Principal Data Scientist to lead the science behind our voucher and incentive programs across food delivery, ride-hailing, and logistics. Our promotional spend reaches millions of consumers weekly, and every rupiah is allocated by models you’ll help design.

This is a hands-on technical leadership role. You’ll set the scientific direction for causal modeling across the incentives portfolio, mentor senior data scientists, and partner closely with product, engineering, and business teams to move key efficiency metrics while growing transactions and retention.

If you're excited about causal work that influences how a large budget gets allocated and drives growth then this role is for you!

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.

* 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.