Senior Data Scientist – MarTech (Measurement & Optimization)
Core
Drive performance marketing efficiency through advanced measurement, optimization, and scalable modeling for budget allocation, incrementality measurement, and bidding.
Role type
Senior IC data scientist (causal inference & marketing optimization)
Builds
Production-grade solutions for budget allocation, incrementality measurement, and bidding
Domain
MarTech / Performance Marketing / Causal Inference
Deliverable
production ML models
Required skills
causal inference, modeling under uncertainty, MMM design, incrementality testing, geo experiments, synthetic control, LTV modeling, statistical rigor, cross-method validation, backtesting, sensitivity analysis, production system design, stakeholder influence
Preferred skills
knowledge transfer, knowledge sharing, narrative translation for non-technical stakeholders, handling ambiguous requirements
Technologies
MMM, geo experiments, synthetic control, LTV models
Responsibilities
Own end-to-end marketing measurement & optimization solutions; Lead ambiguous, high-impact problem spaces from problem framing to production; Define success metrics and modeling approaches when requirements are not clearly specified; Balance methodological rigor with business constraints and timelines; Translate ambiguity into decision frameworks; Convert vague marketing questions into structured, model-driven decision systems; Operate in environments where experimentation is limited or infeasible, and triangulate between MMM, experiments, and observational methods; Make and justify assumptions explicitly, and assess their impact on decisions; Drive decision-making under uncertainty; Provide clear recommendations despite imperfect measurement, articulating trade-offs and confidence levels; Navigate conflicting signals (e.g., attribution vs incrementality vs MMM); Ensure outputs are actionable and aligned with real business constraints (budget caps, pacing, channel dependencies); Lead methodological design, validation, knowledge transfer; Design and evolve frameworks across MMM, incrementality testing (geo experiments, synthetic control), bidding, and LTV; Establish robust validation strategies in the absence of ground truth (cross-method validation, backtesting, sensitivity analysis); Set standards for statistical rigor, interpretability, and reproducibility; Drive internal & external knowledge sharing across Delivery Hero and industry; Drive adoption and stakeholder alignment; Translate complex modeling outputs into clear narratives for non-technical stakeholders; Communicate with stakeholders ranging from squad ICs to tribe leadership, adapting abstraction level appropriately; Handle pushback on model outputs and build trust in methodologies over time; Build production-grade systems; Develop reliable, maintainable solutions with high standards in testing, monitoring, documentation
