Data Scientist

Role Overview

As a Hybrid Data Scientist you will sit at the intersection of high-scale data pipelining and advanced statistical methodology. You will be responsible for the end-to-end lifecycle of Incremental Reach and Audience Measurement products—from architecting Python-based data pipelines to implementing sophisticated Bayesian and Machine Learning models that quantify the lift of Digital media over a Linear TV baseline.

Key Responsibilities

1. Advanced Statistical Modeling (The "Science" Side)

• Incremental Reach Frameworks: * Small-N Datasets: Implement Bayesian Model Averaging (BMA) to cycle through regression combinations, providing robust coefficients and credible intervals when study data is limited.

  • Large-Scale Prediction: Deploy Gradient Boosted Regression Trees (GBM) to identify non-linear patterns and rank the impact of "Reach Drivers" (Media Weight, On-Target %, Frequency).

• Audience Deduplication: Use Maximum Entropy (MaxEnt) models to estimate unique audience reach across fragmented platforms by reconciling census and panel data.

• Additional Frameworks:

  • Mixed-Effect Models: Use Hierarchical/Multilevel modeling to account for nested data (e.g., campaigns nested within specific industry verticals).

  • Causal Lift: Apply Synthetic Control Methods to measure incremental shifts in behavior for campaigns with fixed timeframes where a clean control group is unavailable.

2. Data Engineering & Pipeline Architecture (The "Engineering" Side)

• Python-Centric ETL: Architect and maintain robust data pipelines using Python (Pandas, PySpark) to ingest, clean, and harmonize data from Linear TV logs and Digital ad servers.

• Feature Engineering: Automate the extraction of Base Drivers (GRP, Reach Efficiency, Seasonality) and Custom Drivers (Share of Voice, Flighting) into a supervised learning-ready schema.

• Productionization: Wrap statistical models into production-grade APIs or scheduled containers (Docker/Airflow) to ensure repeatable and scalable measurement.

• Cloud Operations: Manage large-scale datasets within Cloud Data Warehouses (Snowflake, AWS, or GCP), optimizing SQL queries for high-performance analytics.

3. Experimental Design & Methodology

• Control/Test Logistics: Design scientifically valid Control and Test groups, ensuring proper randomization or using Propensity Score Matching to mitigate selection bias.

• Variable Importance: Provide stakeholders with Posterior Inclusion Probabilities to identify which media levers (Duration, Weight, etc.) most consistently drive incremental reach.

• Cross-Media Calibration: Reconcile Linear TV's "One-to-Many" metrics with Digital's "One-to-One" tracking to provide a unified view of the consumer.