Principal Data Scientist - Rewards Promo Loyalty (Rpl)

Gotogroup; Jakarta Pusat; On-site; IDR 700,000,000 - 1,200,000,000; Full time

Job summary

GoTo Group is seeking a Principal Data Scientist to lead causal inference and uplift modeling for our promotions across services in Indonesia. You will own the end-to-end modeling lifecycle, mentor senior data scientists, and partner with product and engineering to drive growth and efficiency.

You'll design scalable uplift estimators, experiment designs, and budget-constrained allocation strategies while ensuring rigorous evaluation and clear communication of tradeoffs to leadership.

Qualifications

• 8+ years in data science with at least 3+ years in causal inference or uplift modelling in production.
• Strong expertise in heterogeneous treatment effect estimation.
• Experience optimizing promotions and incentives with evolving constraints.

Responsibilities

• Lead end-to-end modeling and estimation for promotion optimization including elasticity and budget-constrained allocation.
• Advance causal inference stack with experimental and quasi-experimental designs.
• Mentor data scientists and align model outputs with business budget decisions.

Tools

Meta-learners

Causal forests

DR-learner

Deep uplift

Job description

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

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.

About GoTo Group

GoTo is the largest digital ecosystem in Indonesia. GoTo's mission is '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.