Data Scientist

Job Description **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 TVs "One-to-Many" metrics with Digitals "One-to-One" tracking to provide a unified view of the consumer. Qualifications - **Experience:** 3-6 years of statistical model development and Mastery of Python (specifically for data manipulation and ML) and advanced SQL. Experience with PySpark or Dask for distributed computing is a plus. - **Statistical Mastery:** Proven experience with GBM (XGBoost/LightGBM) and Bayesian Frameworks (e.g., PyMC, Stan, or R-BMA) among other Data Science models. - **Media Knowledge:** Understanding of Linear TV vs. Digital dynamics, including Reach/Frequency, GRPs, and Deduplication logic. - **Education:** Bachelor s or Master s in a quantitative field (Statistics, Computer Science, Economics) or equivalent professional experience. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying. Note: This is a third party job (Aggregated by careeruplift.ai). Shortlisting and Final hiring decision & process is handled by the company.