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Senior Data Product Manager - Machine Learning
FanDuel
LocationAtlanta, Georgia, United States, Atlanta, New York City
Last observed2026-06-13 05:25:15.123433
Job idtusk-fanduel:greenhouse:7675336
THE POSITION Our roster has an opening with your name on it We are looking for a Product Manager to join our Data Products team within our Technology. This is a key role in a fast-paced environment working with teams and stakeholders across the business to drive the development and enhancement of our Data Science and Machine Learning products. In this role, you'll drive the evolution and adoption of our ML Platform, including experiment tracking, model lifecycle management, observability, and feature engineering capabilities that enable data scientists to build, deploy, and monitor models at scale. You'll also own the product strategy for ML-powered consumer applications — today focused on search relevance and ranking across the FanDuel product suite, with future scope expanding across personalization, recommendations, and beyond. In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs. THE GAME PLAN Everyone on our team has a part to play You will take ownership of data products that deliver key insights into our business and drive future business decisions Lead elicitation of requirements, in the form of user stories & acceptance criteria, prioritizing the product backlog to streamline the execution of program priorities Work closely with cross-functional teams, including data scientists, engineers, and business stakeholders, to ensure our platform aligns with business objectives and delivers advanced machine learning solutions Own the product roadmap for ML-powered consumer features, including search ranking, relevance tuning, and recommendation systems — translating business outcomes into model requirements and working backward from the user experience Partner with Data Science, ML Engineering, and product teams to define and improve relevance signals, evaluation frameworks (offline metrics, A/B experiments, user feedback loops), and deployment criteria for consumer-facing ML features Bring fresh ideas to the table when working to solve business problems, using your commercial understanding to generate innovative solutions Create and maintain user guides, technical documentation, and best practices for the Machine Learning platform, including tooling for experiment tracking, model deployment, feature engineering, and observability Monitor platform performance, model accuracy, and data quality. Identify and address issues to continuously improve platform efficiency Play a key role within the PM community here at FanDuel, sharing your industry best practice and fostering a culture of knowledge sharing and cross-skilling THE STATS What we're looking for in our next teammate 5-8 years of experience as a Product Manager, Product Owner, or Data Scientist delivering impactful data products, with a proven track record of successful project delivery and stakeholder satisfaction. Experience working with data warehouse and data science technologies, including platforms such as Python, PySpark, Databricks, AWS, and MLflow ensuring efficient data processing and management. Sufficient technical fluency in ML frameworks to partner effectively with ML engineers on model development and deployment requirements; hands-on experience is a plus Expertise in using Databricks for scalable data engineering and machine learning workflows, leveraging its collaborative environment and optimized Spark clusters for accelerated development and deployment cycles. Strong proficiency in SQL for querying and manipulating large datasets, combined with advanced programming skills in Python for data preprocessing, feature engineering, and model development tasks. Hands-on experience with MLflow for experiment tracking, model registry, and model lifecycle management, including integration with Unity Catalog for centralized governance and versioning Familiarity with ML observability a
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