Senior Research/Data Scientist, Growth

About Upstart

Upstart is the leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than 80% of borrowers are approved instantly, with zero documentation to upload.

Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California; Columbus, Ohio; and Austin, Texas.

Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you!

The Team

Upstart’s Growth Team  engages in marketing and borrower acquisition through direct mail (DM) campaigns and targeted loan offers on partner sites, leveraging machine learning to drive growth. The Growth ML team develops models to optimize borrower targeting, predict conversion probabilities, and refine pricing strategies across these channels. We’re also beginning to explore ML opportunities in other parts of marketing - specifically in cross-selling our newer products (HELOC and Auto Refi) to existing customers using surfaces within our site.

As the Research Scientist or Data Scientist - Growth  at Upstart, you will  run and analyze experiments, conduct one-off analyses, and build models to help determine which of these products we should market to which borrowers. This is a net new area of research for the growth team, so you’ll have the opportunity to collaborate with stakeholders on use-cases and solutions and build net new capabilities from zero to one.

How you’ll make an impact

• Improving marketing conversion rates and cross-sell volume through better targeting and campaign optimizations.
• Launching and analyzing A/B tests or causal inference studies to generate actionable insights that lead to measurable improvements.
• Collaborate with and influence partner teams, ensuring that data-driven recommendations lead to strategic business decisions.
• Build scalable data processes, reducing bottlenecks in analysis and execution.
• Quickly prototype new models that can drive cross-sell decisions and improve new product growth. 

Minimum Qualifications 

• Strong academic credentials with a master's degree in statistics, mathematics, economics, computer science, or other quantitative areas of study
• Knowledge of causal inference techniques and experimentation
• Programming skills in Python 
• Proficiency in a broad array of mathematical, statistical learning and machine learning concepts and applications
• Ability to proactively and effectively communicate results and methodologies to both technical and non-technical stakeholders 
• Strong sense of intellectual curiosity balanced with humility, drive and teamwork
• Experience solving real-world DS or ML  problems at a tech company
• Experience working as part of a diverse, cross-functional team.

Preferred Qualifications

• PhD in  in statistics, mathematics, economics, computer science, or other quantitative areas of study
• Experience in marketing applications, modeling to drive cross sell, propensity model, and/or recommendation systems
• [DS] Expertise in causal inference and experimentation
• [RS] Expertise with causal ML techniques 
• [RS] Full-stack expertise with all steps of the modeling process from ideation to productionalizing code; OR deep expertise in either statistical modeling or machine learning
• [RS] Knowledge of MLops, pipelines, and engineering architecture

Position Location - This role is available in the following locations: Remote

Time Zone Requirements - This team operates on the East/West Coast time zones. 

Travel requirements - As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

What you'll love: