Partner with Applied ML engineers to define the requirements for discovery and ranking algorithms.
Identify and prioritize the data signals (user intent, historical behavior, market urgency) that will improve model accuracy and conversion.
Establish KPIs for ensuring that the roadmap translates directly into business growth.
Act as the Product Owner for our internal experimentation tools.
Define the technical requirements for automated triggering, variance reduction, and real-time result calculation.
Collaborate with Engineering to design and enforce a robust event-tracking schema.
Manage the lifecycle of data products from ingestion and dbt transformation to final consumption in the app or ML models.
Serve as the technical translator between the Data/ML teams and the core Product, Design, and Engineering (PDE) squads.
Lead the evaluation of third-party technical vendors (CDPs, ML Ops tools, Analytics stacks) vs. building proprietary in-house solutions.
Work with the XFN Stakeholders to ensure the technical data roadmap is synced with other teams.

Requirements

6+ years as a Technical PM, Data Product Manager, or Data Scientist with a heavy focus on product shipping.
You can "speak engineer." You are comfortable discussing APIs, model latency, training-serving skew, and data pipeline orchestration (Airflow/dbt).
Solid understanding of machine learning fundamentals (Supervised vs. Unsupervised, Collaborative Filtering, Neural Networks) and how to evaluate model success.
Expert SQL. You don't wait for a report; you dive into the data yourself to validate a technical hypothesis.
You understand that technical excellence is useless if it doesn't solve a fan's problem. You prioritize technical debt vs. new features with a "value-first" mindset.
Degree in Computer Science, Engineering, Data Science, or a related technical field.

Benefits