This role requires the ability to work lawfully in the U.S. without
employment-based immigration sponsorship, now or in the future.
Proven record leading data science for data, audience, identity, targeting, or measurement products in the advertising, media, or marketing ecosystem, where models are packaged, priced, sold, and renewed by external buyers
Deep, hands-on expertise in predictive modeling on consumer-level data at scale, including propensity and response modeling, classification, gradient boosting, regression, calibration, segmentation and clustering, and lookalike modeling
Strong grounding in measurement and causal inference, including incrementality testing, matched and synthetic controls, test-and-holdout design, and the practical limits of attribution and media mix approaches
Working knowledge of identity and data collaboration: household and device graphs, onboarding and match rates, clean rooms (Snowflake, LiveRamp, AWS, or equivalent), identity namespaces such as RampID and UID2, and the use of licensed third-party data in modeling
Command of the full data science lifecycle in production, from feature engineering through training, validation, deployment, monitoring, and iteration, at large dataset scale (billions, even trillions of records); experience designing feature stores and scoring pipelines that serve audiences and partner deliveries
Hands-on proficiency with Python, SQL, and distributed data processing (Spark or equivalent); experience running ML workloads on Snowflake, Databricks, AWS, or equivalent cloud platforms
Solid working knowledge of deep learning and representation learning, including embeddings, sequence models, and transformers, sufficient to direct an embedding roadmap with external partners, set its evaluation standards, and put embeddings to work as features in downstream models
Familiarity with privacy-preserving analytics and consumer data regulation, including aggregation thresholds, differential privacy, and consent management
Track record of building and growing high-performing data science teams, ideally from an early stage, and recruiting senior talent in competitive markets
Bachelor's Degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field
Experience leading applied data science or analytics teams that build data, audience, identity, or measurement products: 7 years
Hands-on predictive modeling, machine learning, or measurement science on consumer-level data at scale: 10 years
