About the role

Lead Product Analyst identifying product, operational, and business opportunities across Match Group's platforms. Collaborate with teams to drive improvements in outcomes for the consumer dating ecosystem.

Responsibilities

Define and evolve KPIs for Core Product, including platform health, adoption, quality, and business impact across brands.
Partner with Product Managers to frame problems, size opportunities, define success metrics, and inform roadmap priorities.
Lead experiment design, feature measurement, cohort analysis, and post-launch evaluation.
Analyze large, complex datasets to uncover product insights, risks, and growth opportunities across brands and systems.
Partner with Engineering, Data Engineering, and BI to improve instrumentation, metric definitions, data models, and self-service analytics.
Build dashboards and analytical frameworks that help teams monitor performance and make faster decisions.
Translate ambiguous questions into clear analysis, practical recommendations, and compelling narratives.
Serve as a lead individual contributor, raising the bar for analytical rigor, measurement, and storytelling.
Mentor others and help strengthen analytics standards and capabilities across Core and partner teams.

7+ years of experience using quantitative analysis to drive product and business decisions in digital product environments.
4+ years of experience directly supporting Product Managers or embedded within a product organization.
Experience owning product metrics and evaluating feature performance through experimentation, funnel analysis, retention analysis, and segmentation.
Strong technical analytics skills across large datasets, from event-level data to executive reporting.
Strong understanding of product analytics in multi-sided or B2B2C ecosystems.
Experience improving instrumentation, event schemas, and data models to increase data quality and speed to insight.
Strong grounding in statistics and experimentation methods.
Advanced SQL skills and proficiency in Python or R.
Experience with BI and visualization tools such as Databricks, Looker, or similar.
Strong communication skills and the ability to influence both technical and non-technical stakeholders.
Experience mentoring others and raising the analytical quality bar across a team.
Familiarity with user growth, trust and safety, and operational tooling analytics preferred.