Applied Scientist

The Team:

Upstart’s Machine Learning Growth team develops models that help optimize borrower acquisition across marketing channels. The Direct Mail team focuses on causal machine learning models that predict incremental conversion and help prioritize prospects, and its scope is expanding beyond Personal Loans into Home Equity Line of Credit (HELOC), email marketing, digital, and other marketing use cases.

The Role:

As an Applied Scientist at Upstart, you will improve existing models and develop new approaches that expand the team’s impact across marketing channels. You will work across business-oriented analysis, machine learning research, experimentation, and production-ready modeling, partnering primarily within Machine Learning and with Growth and Marketing Platform Engineering stakeholders.

How you’ll make an impact:

• Analyze historical model and campaign performance to identify opportunities to improve model effectiveness and marketing outcomes.
• Develop and evaluate machine learning models, including researching new features and model architectures, to improve prospect selection and support new marketing use cases.
• Design statistically rigorous experiments and model evaluations to measure causal impact, optimize campaign outcomes, and inform decisions.
• Navigate complex, messy datasets and build reusable data pipelines, metrics, and analytical approaches that enable efficient model development and analysis.
• Partner with Machine Learning, Growth, and Marketing Platform Engineering teams to translate business problems into research agendas, intermediate milestones, and production-ready solutions.
• Expand machine learning capabilities beyond direct mail into lifecycle, email, digital, and other emerging marketing channels.

Minimum Qualifications:

• Master’s Degree in Mathematics, Statistics, Economics, Operations Research or a related field
• Experience applying statistical and machine learning methods to modeling or data science problems.
• Experience using Python for data analysis, data preparation, and machine learning model development.
• Experience with causal inference and experimental design, including statistically rigorous evaluation of model or experiment performance

Preferred Qualifications:

• PhD in Mathematics, Statistics, Economics, Operations Research or a related field (or its equivalent)
• Knowledge of causal machine learning methods and modeling approaches.
• Ability to translate broadly scoped business problems into structured research questions, analyses, and modeling approaches.
• Experience working across exploratory data analysis, machine learning research, experimentation, and production model development and scaling.
• Ability to interpret complex experimental or modeling results and translate findings into actionable recommendations.
• Experience applying machine learning to marketing, customer acquisition, lifecycle, or other growth use cases.

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 or Canada (outside of Quebec) but are expected to spend high quality time in-person collaborating via regular onsites and in-person meetings. The onsite cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.