Senior Data Scientist, Product Data Science; Mailchimp

The Mailchimp Product Data Science team turns product and customer data into insights and models that shape Mailchimp's product strategy. We work as thought partners with Product, Design, Engineering, Marketing, Finance, and Pricing to drive data-driven decisions through experimentation, causal measurement, and deep customer analytics.

We're looking for a Senior Staff Data Scientist to be an analytical leader for Mailchimp's product growth. In this high-impact individual contributor role, you'll set the measurement vision across the full customer journey: sign-up and onboarding, activation, conversion to paid, engagement, and long-term retention. The role raises the scientific bar for how we experiment and size opportunities, and it shapes product and business strategy at the senior leadership level.

It's also hands-on, focused on the highest-leverage problems and on helping the team adopt AI-native workflows that let us move faster.

This role is based in New York, NY / Mountain View, CA / Atlanta, GA under Intuit's hybrid model, 3 days per week in office.

• Influence product strategy: Be a strategic thought partner to senior Product and business leaders. Shape Mailchimp's growth roadmap with deep dives into customer segmentation, onboarding and activation funnels, feature adoption, pricing and packaging, and retention.
• Set the analytics vision: Own the measurement framework, north-star and guardrail metrics, and leading indicators for the self-serve customer journey, from free and trial sign-ups to paid conversion, bookings, and recurring revenue.
• Lead experimentation and causal measurement: Drive a culture of rigorous, iterative experimentation. Design complex tests (A/B/n, sequential, holdouts, long-running), apply Bayesian and frequentist inference, and use causal inference methods such as difference-in-differences, propensity score matching, and synthetic control where randomized tests aren't possible.
• Size and prioritize opportunities: Build opportunity-sizing and forecasting frameworks that turn product initiatives into incremental revenue impact, accounting for retention curves and the winner's curse, so leadership can prioritize investment.
• Uncover growth opportunities: Use first-principles thinking and exploratory analysis to find untapped levers across the funnel, including in-product onboarding, lifecycle marketing, paid media, and plan upgrades.
• Shape the AI-native roadmap: Partner with Product and Engineering to measure AI-powered features in Mailchimp. Build ML and LLM-based solutions such as propensity models, personalization, and automated insights for hard product problems.
• Raise the scientific bar: Build reusable analytical frameworks, governed metric definitions, and self-serve tools that improve decision-making mpion strong instrumentation and data quality.
• Lead and mentor: Be a technical lead for the Product Data Science team. Coach and grow senior and staff data scientists, review their methods, and model executive-ready storytelling.

• 8+ years in data science, product analytics, or a related field, with a proven record of driving product strategy and measurable business impact.
• Experience in B2B SaaS, subscription, or freemium/self-serve businesses strongly preferred. Familiarity with marketing technology or small-business customers is a plus.
• Expert SQL and data modeling skills on cloud data warehouses (Big Query preferred), plus advanced Python or R.
• Deep expertise in experimental design and causal inference, with the judgment to balance statistical rigor against speed of decision-making.
• Track record of sizing opportunities and forecasting revenue impact, including funnel, cohort, and retention analysis.
• Hands-on experience applying machine learning (classification, regression, uplift or propensity modeling) and LLM/GenAI workflows to real product problems.
• Proficient in an AI-native data science tech stack (for example, Claude, Cursor, or Codex with MCPs, CLIs, and APIs) and eager to keep building how the team works with AI.
• Excellent executive communication. You turn complex analysis into clear narratives and recommendations, and you've influenced senior leaders across Product, Marketing, and Finance.
• Experience leading ambiguous, cross-functional work where the question, metric definitions, or methodology have to be set before the analysis can start.
• Demonstrated mentorship and technical leadership of other data scientists.
• Bachelor's or graduate degree in a quantitative field…