VP, Data Science

The Knot Worldwide•New York, NY

16h•Hybrid

About The Position

The VP of Data Science & Analytics will lead experimentation, business intelligence, and advanced analytics across our global two-sided marketplace. This role is accountable for driving measurable business outcomes — including growth in couple engagement, marketplace liquidity, vendor ROI, and long-term customer value. You will own the company’s experimentation strategy and enterprise BI function, ensuring executives and frontline teams alike have trusted, actionable insights. This role will partner closely with data engineering to build reliable, scalable end-to-end data pipelines that power experimentation, analytics, and executive reporting. You will be the architect of our measurement engine — turning data into durable competitive advantage in a complex, two-sided marketplace. This is a highly visible leadership role reporting to the CPO and partnering across Product, Engineering, Finance, Marketing, and Sales.

Requirements

15+ years in Data Science, Analytics, or quantitative leadership roles.
Experience leading BI and analytics in a large, global organization.
Demonstrated success operating within a two-sided marketplace or platform business.
Proven experience owning experimentation strategy and delivering measurable business impact.
Experience partnering with C-suite and Board stakeholders.
Deep knowledge of experimental design, causal inference, and statistical modeling.
Experience building scalable experimentation and analytics ecosystems.
Strong fluency in SQL and modern data tools (e.g., Snowflake, Looker, Sigma, Tableau).
Strong understanding of marketplace unit economics and LTV modeling.
Experience building and scaling high-performing Data Science and Analytics teams.
Strong cross-functional collaboration skills in matrixed environments.
Ability to translate complex quantitative analysis into clear business insights.

Responsibilities

Define and scale experimentation strategy across a complex two-sided marketplace (couples and vendors).
Ensure rigorous A/B testing, incrementality measurement, and causal inference across growth, monetization, ranking, and lifecycle initiatives.
Build frameworks that account for cross-side marketplace effects and long-term LTV impact.
Establish clear accountability for experimentation outcomes tied to business performance.
Own executive dashboards and enterprise reporting from Board-level metrics to team-level KPIs.
Develop and maintain a trusted metrics layer with clear governance and definitions.
Improve forecasting, driver trees, and performance diagnostics tied to CPAs, GMV, and marketplace health.
Enable scalable self-serve analytics capabilities across the organization.
Lead applied data science across personalization, marketplace dynamics, pricing, segmentation, and lifecycle modeling.
Develop robust LTV and marketplace health models.
Partner with ML teams to ensure strong model evaluation and business impact measurement.
Partner closely with Data Engineering to design scalable experimentation infrastructure and data pipelines.
Influence architecture decisions without directly owning DE.
Serve as a strategic advisor to executive leadership on data-driven growth strategy.