Principal Data Scientist

Proven experience in marketing and media analytics, with a focus on attribution modeling, causal inference, and media mix optimization.
Strong background in machine learning, econometrics, and statistical modeling with applications in marketing measurement.
Expertise in Python, R, SQL, and cloud platforms (AWS, GCP, or Azure) for scalable modeling and data processing.
Experience with experimental design and incrementality testing in digital and traditional media channels.
Ability to communicate complex data science concepts to both technical and non-technical stakeholders, influencing business decisions.
Ph.D. or Master’s degree in a quantitative field (e.g., Statistics, Economics, Computer Science, Applied Mathematics).

Lead the development and refinement of attribution models (MTA, MMM) to quantify the impact of media investments across channels (digital, social, TV, search, etc.).
Advance causal inference techniques to improve media measurement, including uplift modeling, propensity scoring, and Bayesian methods.
Develop forecasting models to predict marketing performance, budget allocation impacts, and long-term customer acquisition trends.
Enhance ad testing methodologies, including A/B testing, synthetic control experiments, and incrementality testing, to measure media effectiveness accurately.
Collaborate with marketing, finance, and analytics teams to translate model insights into business strategies that drive ROI improvements.
Optimize media spend through AI-driven decisioning, leveraging reinforcement learning, Bayesian optimization, and econometric modeling.
Stay at the forefront of AI & ML innovations in marketing measurement, driving technical advancements and thought leadership within the team.
Work closely with data engineers to scale and automate model deployment, integrating ML solutions into production environments.

No preferred qualifications provided.