VP Analytics

About the Role 

The VP, Analytics owns Skillz Platform analytics across SDK and Arena, consolidating people currently split across separate reporting lines into a single function accountable for measurement integrity, experiment quality and decision velocity. The function is organizationally independent of every function it scores — including Product, Growth and Strategy.

Responsibilities 

• Own Skillz Platform analytics across SDK and Arena, leading roughly ten people across four sub-functions with four direct reports.

• Establish and govern a single source of truth: the metric dictionary, the Snowflake semantic-layer contract, definitional change control, and reconciliation between the network P&L and venue scorecards.

• Specify and land the lifecycle marker that install-to-second-payment, install-to-stable and CAC-to-stable all depend on.

• Establish holdout-based incrementality as the default standard for campaigns, promos, incentives and channel spend, and refuse reads that do not have one.

• Serve as the independent scoring authority for the economy — cash, Z, Ticketz and matchmaking — against the guardrail bands set by VP Strategy.

• Ensure every stage of the profit-driver chain and both product surfaces have a named analytics owner, a live baseline and a defined cadence.

• Build and lead the Competitive & Market Intelligence function, giving leadership an external benchmark for every internal target.

• Partner as a peer with Product, Growth, Engineering, Data Engineering, Player Trust and FP&A; represent analytics in quarterly planning and the weekly leadership ritual.

• Hire, develop and retain the analytics bench; build a leadership layer and remove key-man risk on integrity and reconciliation.

Key Competencies 

• Measurement rigor under commercial pressure — can hold a defensible number when a senior stakeholder wants a different one, and knows the difference between a directional read and a causal one.

• Org design and economic thinking — structures analytics around the business's economic model rather than around features or requesters; has consolidated fragmented analytics functions before.

• Experimentation and causal-inference depth — holdout design, incrementality, cannibalization testing, and the judgment to know when a clean experiment is not worth the cost.

• Independence without isolation — scores the functions they partner with, and keeps the partnership intact while doing it.

• Executive communication — translates measurement into decisions for an SLT audience without oversimplifying or hiding behind method.

• Player-economy intuition — reasons fluently about deposits, wallets, incentive burn, tier migration, LTV and matchmaking fairness.

Experience 

• 14+ years in analytics or data science with 6+ years leading multi-team organizations, including managers reporting in.

• Owned analytics for a consumer product with a rea