Responsibilities

• Design and analyze randomized and quasi-experimental tests (holdouts, geo-tests, RCTs) to measure advertising incrementality and lift.
• Build and maintain causal models (difference-in-differences, synthetic controls, hierarchical Bayesian, uplift modeling) and marketing mix models (MMM) for multi-channel attribution.
• Develop and productionize scalable end-to-end pipelines for event-level ad exposure, conversions, and offline-sales ingestion (ETL/ELT, validation, monitoring).
• Work with data engineering to keep measurement datasets clean, deduplicated (identity resolution), and privacy-compliant.
• Own feature engineering, model training, validation, and deployment in Python/R and cloud environments (BigQuery, Snowflake, Dataproc/AWS/GCP).
• Produce clear, actionable dashboards and executive-ready insights for product, media, and client teams; present findings to stakeholders.
• Implement automated reporting and CI/CD for model retraining and performance monitoring; establish measurement governance and documentation.
• Stay current on the ad-tech/measurement ecosystem (ATtribution frameworks, walled gardens, ID solutions) and recommend measurement strategy changes.

Requirements

• 3+ years experience building statistical or ML models in ad-tech, marketing analytics, agency measurement, or a related data science role.
• Strong statistics/causal inference fundamentals: experimental design, hypothesis testing, regression, hierarchical models.
• Proficient in Python (pandas, scikit-learn, PyMC/Stan or equivalent) and SQL; experience with R is a plus.
• Hands-on experience with event-level ad/exposure and conversion data, logs from ad servers/DSPs, or retail/point-of-sale integration.
• Experience working with cloud data warehouses (BigQuery, Snowflake, Redshift) and ETL tooling (Airflow, dbt, Kafka).
• Excellent written and verbal communication; proven ability to translate technical results to non-technical stakeholders.

Preferred

• Experience with incrementality platforms or approaches (e.g., Measured, experimentation platforms, proprietary lift frameworks).
• Familiarity with marketing mix modeling (time-series, regularized regression, Bayesian MMM).
• Experience with causal ML / uplift modeling and Bayesian inference tools (PyMC3/4, Stan).
• Knowledge of identity resolution, privacy-preserving measurement (privacy regulation awareness, cohort-based measurement, differential privacy concepts).
• Experience deploying models to production (Docker, CI/CD, MLOps patterns) and instrumenting model monitoring.
• Background working with brand/performance media, cross-channel measurement, and agency/client workflows.