Staff Data Scientist - Economist

About EvenUp

EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more.

We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact.

About the Analytics Team

The Analytics team at Evenup plays a critical role in driving data-informed decision-making across the organization. We partner closely with Product, Engineering, Operations, and Executive teams to uncover insights, build scalable data solutions, and enable a culture of data-driven decision making and continuous improvement. Our team is responsible for everything from foundational data modeling and reporting to advanced analytics and forecasting.

Role Overview: Staff Data Scientist / Economist

We’re looking for a Staff Data Scientist / Economist to join our fast-paced, data-driven team. You will be a thought partner to senior leadership and shape the way we use economics and data science to drive long-term value and shape our product and monetization strategy. You’ll play a key role in understanding usage patterns in our platform via funnel analysis, causal analytics, etc, to ultimately improve activation, engagement and prevent churn.

The ideal candidate for this role will work at the intersection of data, economics, and strategy to drive high-impact decisions across pricing, growth, and product monetization. You will apply your expertise in causal inference, econometrics, and behavioral analysis to answer some of the most critical questions in our business.

This is a hands-on IC role with outsized ownership, ideal for someone who thrives on solving ambiguous problems, building clean data narratives, and pushing organizations toward better decisions with data.

Responsibilities

• Translate complex datasets into clear insights with strategic recommendations that drive measurable actions and outcomes to inform product direction and business priorities
• Partner with Product, Engineering, Ops teams and Executives to define KPIs and lead the analytics strategy for key product areas, including causal inference, cohort analysis, retention modeling and experimentation design
• Develop forecasts for ARR, conversion, churn, and usage using economic and behavioral data
• Collaborate with data engineering, analytics, and product to shape best practices in experimentation, forecasting, and decision support
• Help build and evolve our analytical foundation, including event instrumentation, metric frameworks, and scalable self-serve analytics tools
• Mentor other analysts and raise the bar for data storytelling, analytical rigor, and stakeholder influence across the org

Qualifications

• Deep expertise in both economics and applied data science, and the ability to frame business questions into well-scoped analytical or modeling problems.
• 6+ years of experience in data science, applied economics, or a related field (industry or post-PhD academic research), ideally in high-growth tech environments.
• Advanced degree (PhD preferred) in Economics, Econometrics, Statistics, or a related quantitative field
• Proven ability to identify and measure causal business impacts using appropriate statistical techniques, especially in complex or observational settings
• Strong statistical modeling skills (time-series, forecasting, survival analysis, Ba

Job Details

• Salary: $183K - $275K
• Location Type: Hybrid
• Employment Type: FullTime

Location

• This is a hybrid role with the expectation of working at least 3 days a week from one of our office hubs in San Francisco or Toronto.