Responsibilities

• Design, build, and maintain custom audience models (propensity scoring, lookalike modeling, custom segmentation, clustering) using statistical and ML techniques
• Develop and deploy internal audience tools and data products that increase the speed, accuracy, and scalability of the Audience Strategy team’s output
• Operate across multiple clean room and data collaboration environments (Epsilon, LiveRamp, Snowflake, Habu, AWS Clean Rooms, retailer-specific environments) as a platform-agnostic technical resource
• Build automated pipelines for audience creation, refresh, and distribution across activation platforms
• Own the technical execution of Test & Learn roadmaps co-developed with Audience Strategists, from hypothesis design through measurement and iteration
• Evaluate and prototype emerging AI/ML applications for audience development, including generative AI use cases, predictive modeling enhancements, and automation of manual workflows
• Serve as the technical voice in client-facing meetings alongside Audience Strategists, translating data science concepts and platform capabilities into business-relevant language
• Provide consultative guidance on clean room readiness, first-party data strategy, identity architecture, and data collaboration best practices
• Advocate for deterministic identity solutions and advanced data use cases, including partnership with investment teams to support campaign activation
• Perform audience analysis to extract actionable insights and inform strategic activation recommendations

Requirements

• 4-6 years of experience in digital marketing, data science, or marketing technology, with at least 3 years in a hands-on data science or audience architecture role
• 2+ years of experience working in data lake and/or clean room technologies
• Advanced proficiency in SQL and Python (NumPy, pandas, scikit-learn).
• Demonstrated experience with statistical modeling techniques: logistic regression, clustering (k-means, hierarchical), decision trees, propensity modeling, and dimensionality reduction
• Hands-on experience with at least two of the following: data clean rooms (Epsilon, LiveRamp, Habu, Snowflake, AWS Clean Rooms), CDPs (Adobe, Segment, mParticle, Hightouch), or customer data warehouses (Snowflake, BigQuery, Databricks)
• Working knowledge of identity resolution frameworks (deterministic, probabilistic, unified ID solutions) and how they apply to media activation and measurement
• In-depth technical knowledge of the ad tech and martech ecosystem: ad servers, DSPs, SSPs, verification vendors, dynamic creative, streaming/CTV, retail media networks
• Strong client-facing communication skills. You can present to a room of senior marketers and make complex technical concepts stick.
• A strong opinion when you see an audience data strategy that is too broad, too narrow, or lacks clear linkage to business outcomes
• Proven ability to build something from scratch, whether a model, a tool, or a process. You ship.
• Embody a can-do attitude and a “figureoutability” mindset. Day 1 readiness is the expectation.

Preferred

• Experience in an agency or holding company environment (media, creative, or data-focused)
• Exposure to ML model deployment, MLOps, or pipeline automation (Airflow, dbt, cloud-native orchestration)
• Familiarity with API development or lightweight application building (Flask, FastAPI, Streamlit, or similar)
• Startup or entrepreneurial experience, particularly in building products or capabilities from zero
• Experience with retail media networks and their measurement/audience infrastructure