Principal Data Scientist - Rewards Promo Loyalty (RPL)
What You Will Do
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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.
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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.
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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).
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Formulate and solve budget-constrained allocation under fairness and dynamic business constraints — from LP/MILP to greedy or Lagrangian methods where they scale better.
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Mentor and technically guide a team of data scientists; set standards for model evaluation, documentation, and scientific review.
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Partner with business to turn model outputs into budget decisions, and communicate tradeoffs (subsidy efficiency vs. growth) clearly.
What You Will Need
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8+ years in data science or ML, with 3+ years focused on causal inference or uplift modeling in production settings.
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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.
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Hands-on experience optimizing promotions, pricing, or marketing incentives with evolving constraints and measurable business outcomes.
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Strong grounding in experimentation and observational causal methods — propensity weighting, instrumental variables, synthetic control, difference-in-differences.
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Experience with constrained optimization (MILP, Lagrangian methods, etc.) applied to resource allocation.
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Proficiency in Python and SQL; shipping models to production with engineering partners.
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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.
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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
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Publications or open-source contributions in causal ML (e.g., work building on EconML, CausalML, or the uplift literature)
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Experience operating across multiple markets/geographies in Southeast Asia
About the Team
Skills
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