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# Senior Staff Data Scientist, Product Data Science (Mailchimp)

- **Company:** Intuit Inc.
- **Location:** Mountain View, CA, United States
- **Experience:** Expert
- **Salary:** $210,500 - $284,500
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Cloud Database, Cursor, Python (Programming Language), Machine Learning, SQL Databases, Large Language Models, Information Technology, Claude, Mailchimp
- **Published:** November 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7617855c1037c5b0

## About the Role

* 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 (BigQuery 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 such as Statistics, Economics, Computer Science, Mathematics, or Operations Research, or equivalent practical experience.

## Description

* 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 at scale. Champion 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.