Data Scientist

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future. 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 timefram