Back
Mid-level data scientist in Bangalore at a known firm with a popular title and mid experience increases competition.
Requires advanced Bayesian/causal modeling and media measurement expertise, limiting cross-industry transferability.
Requires explicit 3-6 years and specialized Bayesian, ML, PySpark, and productionization skills, so filters are strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end development of Incremental Reach and Audience Measurement products including data pipeline architecture and advanced statistical modeling.
Implement and productionize Bayesian, Gradient Boosted Trees, and Maximum Entropy models for audience measurement and deduplication across Linear TV and Digital platforms.
Design and execute experimental methodologies such as Control/Test logistics and cross-media calibration to provide unified consumer reach insights.
3-6 years experience in statistical model development with expertise in Python for data manipulation and machine learning, and advanced SQL.
Bachelor’s or Master’s degree in a quantitative field (Statistics, Computer Science, Economics) or equivalent professional experience.
Experience with Gradient Boosted Models (XGBoost/LightGBM) and Bayesian frameworks (e.g., PyMC, Stan, R-BMA).
Not explicitly mentioned in the JD: Notice period, onsite/location requirements.
Strong proficiency in architecting scalable Python-based data pipelines and productionizing statistical models using modern tools like Docker and Airflow.
Demonstrated expertise in advanced statistical and causal modeling techniques applied to media measurement, including Bayesian Model Averaging and Synthetic Control methods.
Deep understanding of media metrics and audience measurement concepts bridging Linear TV and Digital media, enabling effective cross-platform data reconciliation and analysis.