Role overview
- Higher ROI and fewer wrong moves through evidence-based allocation and optimization.
- Faster learning cycles via experimentation-backed model iteration.
- Reduced manual decision work through reliable decisioning systems and guardrails.
- A scalable foundation for personalization and automation beyond marketing.
What you'll work on
- Build prediction models (e.g., conversion/CPA forecasts, propensity/LTV, churn risk, creative or placement performance signals) with strong validation and calibration.
- Build decision policies: rules + constrained optimization (and bandits where appropriate) for allocation, placements, pacing, creative rotation, and personalization.
- Design and analyze experiments/incrementality tests to validate models and policies (not just offline metrics).
- Productionize with “MLE-quality”: reproducible pipelines, versioning, monitoring, alerting, and safe rollback; partner closely with Engineering/Data.
- Translate outputs into decision-ready guidance and clear trade-offs (expected impact, uncertainty, constraints).
- Maintain concise documentation of models/policies, data dependencies, and decision logic.
What we're looking for
- Masters Degree in Computer Science, Data Science, Statistics, Mathematics, Econometrics, or Business Informatics (or equivalent proven skill).
- 5–10+ years experience in Data Science / Applied ML, with at least one example of models/policies used in production.
- Proven experience working with engineers on production constraints (monitoring, reliability, safe deployment).
Tags & focus areas
Used for matching and alerts on DevFound Data Science Ai