Senior ML/AI/Data Technical Product Manager
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.
Key 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.
What Skills & Experience Do You Bring?
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.
What We can Offer:
Flexible PTO
Competitive salary & equity package
Monthly Gametime credits for any event ($1,200/yr)
Medical, dental, & vision insurance
Life insurance and disability benefits
Diverse Family-forming benefits through Carrot Fertility
401k, HSA, pre-tax savings programs
Company off-sites and meet-ups
Wellness programs
Tenure recognition
At Gametime pay ranges are subject to change and assigned to a job based on specific market median of similar jobs according to 3rd party salary benchmark surveys. Individual pay within that range can vary for several reasons including skills/capabilities, experience, and available budget.
United States - Pay Range
$174,250—$205,000 USD
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Senior ML/AI/Data Technical Product Manager
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.
Key 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.
What Skills & Experience Do You Bring?
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.
What We can Offer:
Flexible PTO
Competitive salary & equity package
Monthly Gametime credits for any event ($1,200/yr)
Medical, dental, & vision insurance
Life insurance and disability benefits
Diverse Family-forming benefits through Carrot Fertility
401k, HSA, pre-tax savings programs
Company off-sites and meet-ups
Wellness programs
Tenure recognition
At Gametime pay ranges are subject to change and assigned to a job based on specific market median of similar jobs according to 3rd party salary benchmark surveys. Individual pay within that range can vary for several reasons including skills/capabilities, experience, and available budget.
United States - Pay Range
$174,250—$205,000 USD
