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Metro location, mid-level (3–6 yrs), and common Data Scientist title increase applicant competition.
Role requires media-specific reach/deduplication expertise and statistical modeling, reducing cross-industry transferability.
Explicit 3–6 years plus mandatory Python/SQL and Bayesian/GBM skills create strict shortlisting filters.
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Own the end-to-end lifecycle of Incremental Reach and Audience Measurement products, including data pipeline architecture and advanced statistical modeling.
Develop and productionize sophisticated Bayesian and Machine Learning models (e.g., GBM, BMA) to quantify digital media impact over linear TV baselines.
Design and implement robust experimental frameworks and methodologies for control/test groups, audience deduplication, and cross-media calibration.
3-6 years of statistical model development experience with mastery in Python (data manipulation and ML) and advanced SQL.
Bachelor’s or Master’s degree in a quantitative field (Statistics, Computer Science, Economics) or equivalent experience.
Experience with Bayesian frameworks (e.g., PyMC, Stan) and GBM models (XGBoost, LightGBM).
Not explicitly mentioned: Notice period or strict location requirements. Experience with PySpark or Dask is a plus but not mandatory.
Has strong expertise bridging statistical modelling and data engineering, comfortable handling both advanced ML models and scalable data pipelines.
Experienced in media analytics specifically Linear TV and Digital media measurement, including reach, frequency, GRPs, and deduplication techniques.
Able to implement complex experimental designs and translate statistical outputs into measurable business insights for media impact.