Job Description Summary

FanThreeSixty is looking for a Lead Data Product Manager to own the roadmap and prioritization for our data platform's core engine — the integrations, data science models, and insights/reporting capabilities that power a leading fan engagement platform in sports and entertainment. This role sits at the intersection of data strategy and product management: you'll define what data enters and leaves our platform, guide the models and algorithms that turn raw fan data into actionable intelligence, and ensure the insights we surface are accurate, meaningful, and built to scale.

This role reports to the Sr. Director, Product & Data Strategy.

You'll work as a peer to our Lead Platform Product Manager, with a clear division of ownership: you own everything up to the point where data is consumed — the pipelines, the models, the logic, the metric definitions. Platform owns everything a client sees and clicks. Together, you'll ensure that what gets built is both analytically sound and genuinely usable.

Job Description

Integrations roadmap

Prioritize and manage the roadmap for data flowing into and out of the platform, partnering with engineering to sequence integration work against business impact.

Data Science roadmap prioritization

Own prioritization of the Data Science roadmap — deciding what's worth building as a durable capability versus what should be declined or redirected as a one-off request. Translate business questions into model requirements and ensure outputs are interpretable and defensible.

Insights & reporting standards

Define what gets measured, how it's calculated, and what it means — producing clear metric definitions and requirements that downstream teams (including design and platform) build against.

Data quality & technology governance

Partner with engineering and data science to surface and prioritize data quality issues that affect model or reporting reliability. Ensure any infrastructure, tooling, or architecture decision originating from Data Science routes through Tech & Architecture's standard review process, rather than being made independently.

Internal cross-functional translation

Serve as the primary internal bridge between data science/engineering execution and product/business leadership (Sr. Director, Lead Platform Product Manager, executive leadership), translating technical tradeoffs into business terms and vice versa. Client-facing translation of data needs and use cases is owned by the Client Data Strategist — this role's translation work stays internal.

Continuous improvement

Regularly reassess whether our data architecture, models, and reporting standards still fit our clients' evolving needs and the broader industry landscape. No part of the roadmap should be treated as “done” — only as a baseline to keep improving.

• A prioritized, well-justified roadmap for data integrations, models, and reporting that engineering can execute against without ambiguity.
• A measurable reduction in ad hoc, one-off reporting requests going to Data Science, replaced by reusable data products tied to business outcomes.
• Zero Data Science infrastructure or tooling decisions made outside the standard Tech & Architecture review process.
• Metric and model definitions that are documented, defensible, and don't require re-litigation every time they're used in a client-facing context.
• A working cadence with the Lead Platform Product Manager where handoffs (data 12 interface) happen smoothly, without escalation.
• A track record of proactively identifying where our data capabilities need to evolve — not just reacting to requests, but anticipating where the platform needs to go next.

• 5 6 years of product management experience, with meaningful time spent owning data-intensive products, platforms, or data science-adjacent roadmaps.
• Demonstrated ability to write clear requirements for machine learning or statistical models — you don't need to build them, but you need to speak the language well enough to spec them.
• Experience translating raw data/model outputs into metrics and insights that non-technical stakeholders can act on.
• Comfort operating in ambiguity typical of a lean, high-ownership organization — this isn't a role with a large PM bench around you.
• Strong cross-functional collaboration skills, especially the ability to hold a firm line on analytical accuracy while remaining a good partner to design and engineering.
• Demonstrated ability to set priorities and say no to low-value work — comfort pushing back on requests that don't tie to a clear business outcome.

• Experience in sports, entertainment, or fan/customer engagement platforms.
• Familiarity with modern data stacks (cloud data warehousing, ETL/integration tooling).
• Background partnering directly with data science teams on production ML systems.
• Working knowledge of data privacy and compliance considerations sufficient to partner effectively with the Privacy & Compliance Coordinator — this role is not the primary owner of compliance monitoring or interpretation.