GoToGroup - GoTo Group - Principal Data Scientist - Rewards Promo Loyalty (RPL)
Requirements
• 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
Responsibilities
• 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. • 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
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