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# Principal Data Scientist

- **Company:** Upstart
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $182,300.0 - $252,500.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Machine Learning, Recommender Systems, Software Coding
- **Published:** October 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4bd66412058bbc3e

## About the Role

* 8+ years of experience in data science or quantitative roles with direct impact on product or business strategy.
 * Demonstrated mastery of experimental design and A/B testing
 * Deep statistical expertise, including causal inference methods.
 * Strong coding skills.
 * Track record of partnering with non-technical stakeholders to translate data into actionable business decisions, not just delivering analyses.
 * Comfort operating in ambiguous environments where the right questions aren't predetermined, and the ability to shape direction through thoughtful inquiry.
 * Exceptional communication skills, demonstrated ability to move fluidly between strategic questions and tactical details, and excellence in translating complex technical bodies of work into clear narratives.
 * Exposure to ML applications and ability to partner effectively with ML/data science teams on technical challenges.
 * Familiarity with personalization, recommendation systems, or matching algorithms., * Experience with at least one of the following is highly preferred: forecasting, spend allocation optimization, marketing incrementality, or attribution.

## Description

* Shape strategic direction by identifying the gaps and questions others miss. You're not waiting for orders; you're setting the agenda.
 * Be a thought partner to leaders across product, finance, and ML. You help them understand complex problems from first principles and define what actually matters.
 * Design, build, and own tools that are leveraged across the team, such as models for forecasting change, optimizing spend allocation, marketing incrementality, and attribution.
 * Design and execute experiments, from classic A/B tests to complex causal inference analyses, with full ownership of hypothesis development, experiment design, and results interpretation.
 * Build metrics frameworks and measurement strategies, helping stakeholders understand what to optimize for and why.
 * Collaborate with our ML organization on high-complexity technical challenges, bringing statistical rigor and causal reasoning to our work.
 * Translate ambiguous, multifaceted business problems into clear frameworks that guide decision-making.
 * Take ownership of your work end-to-end: from scoping the question, to analysis, to storytelling, to driving organizational change based on insights.
 * Influence without direct authority across engineering, product, finance, and executive leadership, creating clarity and alignment where understanding is incomplete.