Principal Data Scientist (Causal Inference)

TL;DR

Principal Data Scientist (Causal ML): Leading the science behind voucher and incentive programs across food delivery, ride-hailing, and logistics with an accent on causal modeling and budget-constrained allocation. Focus on designing uplift models at scale, advancing the causal inference stack, and optimizing promotional spend for millions of users.

Location: Singapore (Hybrid)

Company

hirify.global is the largest digital ecosystem in Indonesia, providing integrated services in mobility, delivery, payments, and financial services.

What you will do

• Own end-to-end modeling and estimation for promotion optimization, including elasticity, incrementality, and budget-constrained allocation.
• Advance the causal inference stack through quasi-experiment design, debiasing observational data, and variance reduction.
• Design and productionize heterogeneous treatment effect (uplift) models for tens of millions of users.
• Formulate and solve complex budget-constrained allocation problems using LP/MILP or Lagrangian methods.
• Mentor and technically guide a team of data scientists, setting standards for model evaluation and scientific review.
• Partner with business teams to translate model outputs into high-stakes budget decisions.

Requirements

• 8+ years in data science or ML, with 3+ years focused on causal inference or uplift modeling in production.
• Deep expertise in meta-learners (S/T/X/R), causal forests, DR-learner, or deep uplift architectures.
• Strong grounding in propensity weighting, instrumental variables, synthetic control, and diff-in-diff methods.
• Experience with constrained optimization (MILP, Lagrangian methods) applied to resource allocation.
• Proficiency in Python and SQL with a track record of shipping models to production.
• Proven technical leadership at principal/staff level, influencing roadmaps and partner teams.

Nice to have

• Familiarity with off-policy evaluation, bandits, or reinforcement learning for sequential decisions.
• Publications or open-source contributions in causal ML (e.g., EconML, CausalML).
• Experience operating across multiple markets and geographies in Southeast Asia.

Culture & Benefits

• Opportunity to manage promotional spend affecting millions of users weekly.
• Collaborative environment combining ML, causal inference, and optimization.
• High-impact role where scientific answers directly drive financial and business decisions.
• Work within a leading multi-service ecosystem in Southeast Asia.