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Mid LevelFull-timePythonAWSGCPDockerMachine LearningGitData
Salary Range
$16,150 - $21,850 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for a Hybrid Data Scientist to work at the intersection of data pipelining and statistical methodology. This role involves the end-to-end lifecycle of Incremental Reach and Audience Measurement products, from architecting Python-based data pipelines to implementing advanced Bayesian and Machine Learning models.
Key Responsibilities
- βImplement Bayesian Model Averaging for small-N datasets to provide robust coefficients and credible intervals.
- βDeploy Gradient Boosted Regression Trees to identify non-linear patterns and rank the impact of Reach Drivers.
- βUse Maximum Entropy models to estimate unique audience reach across fragmented platforms.
- βUse Hierarchical/Multilevel modeling to account for nested data.
- βApply Synthetic Control Methods to measure incremental shifts in behavior for campaigns.
- βArchitect and maintain robust data pipelines using Python (Pandas, PySpark) to ingest, clean, and harmonize data.
- βAutomate the extraction of Base Drivers and Custom Drivers into a supervised learning-ready schema.
- βWrap statistical models into production-grade APIs or scheduled containers.
- βManage large-scale datasets within Cloud Data Warehouses, optimizing SQL queries for high-performance analytics.
- βDesign scientifically valid Control and Test groups, ensuring proper randomization.
- βProvide stakeholders with Posterior Inclusion Probabilities to identify which media levers drive incremental reach.
- βReconcile Linear TV's metrics with Digital's tracking to provide a unified view of the consumer.
Requirements
- βExperience with Bayesian Model Averaging and Gradient Boosted Regression Trees.
- βKnowledge of Maximum Entropy models and Mixed-Effect Models.
- βFamiliarity with Synthetic Control Methods and experimental design.
- βStrong Python skills with Pandas and PySpark.
- βExperience with ETL processes and feature engineering.
- βProficiency in Docker, Airflow, and Cloud Data Warehouses (Snowflake, AWS, GCP).
- βStrong SQL skills for high-performance analytics.
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