Data Technical Product Manager

Gametime United

About The Position

Live experiences help people cross today’s digital divide and focus on what truly connects us – the here, the now, this once-in-a-lifetime moment that’s bringing us together. To fulfill Gametime’s mission of uniting the world through shared experiences, we make it easy for people to discover and access the live experiences that matter most. With platforms on iOS, Android, mobile web and desktop supporting more than 60,000 events across the US and Canada, we are reimagining the event ticket industry in order to move at the speed of life. Role Purpose As the Senior Technical PM for Data and Applied ML, you sit at the intersection of product vision and engineering execution. You will be the "bridge" that translates complex product goals into technical requirements for our data and machine learning teams. Your mission is to build the intelligent infrastructure—ranking algorithms, recommendation engines, and experimentation platforms—that creates a seamless, personalized experience for every Gametime fan.

Requirements

Experience: 6+ years as a Technical PM, Data Product Manager, or Data Scientist with a heavy focus on product shipping.
Technical Depth: You can "speak engineer." You are comfortable discussing APIs, model latency, training-serving skew, and data pipeline orchestration (Airflow/dbt).
ML Fluency: Solid understanding of machine learning fundamentals (Supervised vs. Unsupervised, Collaborative Filtering, Neural Networks) and how to evaluate model success.
Analytical Power: Expert SQL. You don't wait for a report; you dive into the data yourself to validate a technical hypothesis.
Product Sense: 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.
Education: Degree in Computer Science, Engineering, Data Science, or a related technical field.

Responsibilities

Applied ML & Personalization Roadmap
Model Productization: Partner with Applied ML engineers to define the requirements for discovery and ranking algorithms. You determine the "what" (e.g., personalized event feeds) while they build the "how."
Feature Engineering Strategy: Identify and prioritize the data signals (user intent, historical behavior, market urgency) that will improve model accuracy and conversion.
Business Impact: Establish KPIs for ensuring that the roadmap translates directly into business growth.
Technical Infrastructure & Experimentation
Experimentation Platform (XP): Act as the Product Owner for our internal experimentation tools. Define the technical requirements for automated triggering, variance reduction, and real-time result calculation.
Tracking & Schema Design: Collaborate with Engineering to design and enforce a robust event-tracking schema. You ensure that our data "source of truth" is clean, scalable, and built for complex analysis.
Data Product Lifecycle: Manage the lifecycle of data products from ingestion and dbt transformation to final consumption in the app or ML models.
Strategic Technical Partnership
XFN Liaison: Serve as the technical translator between the Data/ML teams and the core Product, Design, and Engineering (PDE) squads.
Buy vs. Build: Lead the evaluation of third-party technical vendors (CDPs, ML Ops tools, Analytics stacks) vs. building proprietary in-house solutions.
Stakeholder Alignment: Work with the XFN Stakeholders to ensure the technical data roadmap is synced with other teams.