Principal Data Scientist – RPL

GoTo Group

Principal Data Scientist optimizing Gojek's incentives in food delivery, ride-hailing, and logistics. Leading causal modeling and mentoring data scientists for impactful financial growth.

Posted 7/24/2026full-timeSingapore • 🇸🇬 SingaporeLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in causal inference and heterogeneous treatment effect modeling, with a strong focus on production-level implementation and optimization of marketing strategies. Proven ability to lead technical teams and communicate complex model outputs to drive business decisions.

Highest-signal resume keywords

Causal Inference ExpertiseHeterogeneous Treatment Effect EstimationConstrained Optimization (MILP, Lagrangian)Python and SQL ProficiencyTechnical Leadership

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Applicant Tracking System Keywords

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Hard Skills

Causal ForestsMeta-Learners (S/T/X/R)DR-LearnerDeep Uplift ArchitecturesExperiment DesignObservational Causal MethodsBudget-Constrained AllocationUplift/Qini CurvesPolicy-Value EstimationVariance Reduction

Soft Skills

MentoringTechnical GuidanceCommunication

Industry Keywords

MarketplacesRide-HailingFood DeliveryE-CommerceFintech

Tech Stack

Tools & technologies

PythonSQL

About the role

Key responsibilities & impact

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.

Requirements

What you’ll 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

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development