Sr. Product Data Scientist

Skills

About the Role

You will serve as the analytical engine for the Product Management team, owning the most important product analytics questions. Rather than building traditional dashboards, you will tackle complex product questions such as understanding the true impact of GenAI features on retention and user habit formation. You will also architect infrastructure that lets Product Managers query the BigQuery database using natural language, transitioning the product analytics model from a request-and-wait dashboard culture to a real-time, AI-empowered ecosystem. You will co-locate with the Product team in NYC to understand the business deeply, while collaborating with a technical data team based in India.

Requirements

• Demonstrated ability to understand product strategy and key metrics
• 8+ years of experience working in Product Analytics, Data Analytics, or equivalent roles
• Exceptional written and verbal communication skills with ability to present complex data insights to technical and non-technical stakeholders
• Proven ability to build relationships and influence decision-making across cross-functional teams, particularly Product Management
• Knowledge of which statistical tests to apply and how to interpret results
• Expert-level SQL
• Comfort using Python and working with APIs, LLMs, and agentic frameworks
• Curiosity to explore new AI frameworks and experiment with applying them to business problems
• Ability to work with a highly technical remote data team in India while acting as the strategic face of data for NYC product leadership
• Ability to collaborate in real-time with Product teams primarily based in NYC while working effectively with a team in India

Responsibilities

• Co-locate with the Product team to understand the business deeply
• Proactively identify trends, drop-offs, and opportunities across all product features
• Translate complex data analysis into clear, compelling narratives that drive business and product decisions
• Conduct and oversee comprehensive, complex analyses of product usage and adoption
• Identify root causes for trends beyond surface-level metrics
• Act as a thought partner to Product leadership, identifying opportunities and risks through data analysis
• Collaborate with Product Managers to define KPIs and establish success metrics for new features
• Use advanced statistical methods such as cohort analysis, propensity matching, and causal inference to answer executive and investor questions
• Wire up the BigQuery data warehouse to modern LLMs using protocols like Model Context Protocol or native cloud AI tooling
• Partner with the Data Engineering team in India to ensure data schemas are AI-ready

Benefits

• Performance-based bonus
• Equity
• Generous benefits program