Senior Data Scientist

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!

At Upstart, our mission is to expand access to credit using advanced machine learning models that better evaluate true risk. Our Machine Learning team researches and product ionizes all of Upstart’s core predictive models. If data is the new oil, then you’ll be responsible for the refining process that creates the most premium fuel possible. You’ll work in an environment where actionable insights from data are valued and needed, so you’ll be able to see your analysis turn into measurable results for our company and our borrowers.

As a Data Scientist, you’ll leverage technical and business acumen to become an expert in our models and how the models interact with the business. Identify potential problems or opportunities for improvement, communicate these issues to the team, stakeholders and suggest solutions. This involves self‑directed investigation into our business and its data, designing and preparing regular reports, as well as conducting ad hoc analyses for the Machine Learning team, company leadership, and external business partners.

Collaborating with ML scientists, engineers, product managers, and growth marketers, your work will drive data‑informed decisions and measurable business impact.

• Build and refine metrics for model performance and help understand uncertainty around model performance.
• Conduct in‑depth data analyses to uncover growth opportunities and inform pricing strategy.
• Collaborate with engineers and analysts to build scalable data pipelines that enable efficient data access.
• Partner with cross‑functional teams (ML scientists, engineers, product managers, and growth marketers) to align on priorities and drive measurable business impact.
• Develop dashboards and reporting tools to monitor key metrics and track performance.

• Advanced degree in Statistics, Mathematics, Economics, Finance, or a related quantitative field.
• 2+ years of experience in data science, analytics, or related fields.
• Strong understanding of statistical, probability, and machine learning theory.
• Strong proficiency in SQL for querying and analyzing large datasets.
• Experience with Python and data analysis libraries.
• Proven ability to translate complex data insights into business impact and strategic recommendations.

• Strong critical thinking skills and ability to translate general problem statements into actionable solutions.
• Knowledge of statistical methods for measuring uncertainty (e.g., Bayes, Frequentist approaches).
• Experience collaborating with cross‑functional teams including ML scientists, engineers, and product partners.
• Experience in building visualizations and maintaining Dashboards (Plotly or Matplotlib) to track model performance
• Serving as an expert in the areas of experimental testing, Causal Inference, A/B testing or Hypothesis testing

Position location This role is available in the following locations:
Remote

Time zone requirements The 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 still spend high quality time in‑person collaborating via regular…