Principal Data Scientist - RPL

About the Role

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 proving what's actually incremental - and setting the causal bar for an org that moves real money on your answers - this role is for you.

What You Will Do

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.

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.

About GoTo Group

GoTo is the largest digital ecosystem in Indonesia. GoTo's mission is to 'empower progress' by offering technology infrastructure and solutions that help everyone to access and thrive in the digital economy.

The GoTo ecosystem provides a wide range of services, including mobility, delivery, payments, financial services, and technology solutions for merchants. The ecosystem also provides e-commerce services through Tokopedia and banking services through its partnership with Bank Jago.

About Gojek

Gojek is Southeast Asia’s leading on-demand platform and pioneer of the multi-service ecosystem with over 2.5 million driver partners across the regions offering a wide range of services such as transportation, food delivery, logistics and more. With its mission to create impact at scale, Gojek is committed to resolving consumer problems and raising standards of living by connecting consumers to the best providers of goods and services in the market.

About GoTo Financial

GoTo Financial accelerates financial inclusion through its leading financial services and merchants solutions. Its consumer services include GoPay and GoPayLater and serve businesses of all sizes through Midtrans, Moka, GoBiz Plus, GoBiz, and Selly. With its trusted and inclusive ecosystem of products, GoTo Financial is open to new growth opportunities and aims to empower everyone to Make It Happen, Make It Together, Make It Last.

GoTo and its business units, including Gojek and GoToFinancial ("GoTo") only post job opportunities on our official channels on our respective company websites and on LinkedIn. GoTo is not liable for any job postings or job offers that did not originate from us. You should conduct your own due diligence to prevent being victims of any fake job scams, if they did not originate from GoTo's official recruitment channels.

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.