Senior Applied Scientist
Remote · USD 167,700-231,800 · 3+ years experience · Remote · Posted Oct 12, 2026
At a glance
- Experience
- 3+ years experience
- Workplace
- Remote
- Location
- Remote
- Salary
- USD 167,700-231,800 · Base salary
About Upstart
- At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.
- As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.
- We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you.
- If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.
- The Team
- Upstart’s Servicing team supports borrowers after loan origination and works to improve outcomes for investors and borrowers. The team is building data-informed approaches across areas such as collections, loan assistance, payment experiences, and borrower communications, with meaningful opportunity to apply machine learning to problems that have often been addressed manually.
About the role
- As a Senior Applied Scientist on the Servicing team, you will build predictive and causal models and translate their outputs into decisions that improve how loans are serviced.
- You’ll partner with Product, Engineering, and Servicing and Collections Operations to evaluate interventions and shape data-informed approaches to payment plans, loan modifications, communications, and autopay.
Responsibilities
- Develop and evaluate machine-learning models that predict borrower repayment behavior and other servicing outcomes.
- Apply causal inference and experimentation to measure the impact of servicing interventions, including loan modifications and payment assistance.
- Translate model predictions and treatment-effect estimates into practical decision algorithms and actions.
- Partner with Product and Engineering on payment experiences, autopay, and borrower communications, and with Servicing and Collections Operations on collections strategy and agent-performance insights.
- Identify and develop machine-learning opportunities in servicing areas that have historically relied on manual optimization.
- Explain analytical choices, tradeoffs, and findings to technical and non-technical partners to inform high-stakes operational and product decisions.
Requirements
- Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
- Experience applying causal inference methods and experimentation to real-world decisions in an applied science or data science context.
- Applied statistics, probability, and experimental-design expertise used to build or evaluate models or interventions.
- Production-quality coding experience in Python, including data manipulation and algorithm implementation.
- 3+ years of experience conducting machine-learning, statistical-modeling, or applied quantitative research in an academic or industry setting.
Preferred qualifications
- PhD in a quantitative field.
- Experience with payment products or payment experiences, such as autopay, recurring payments, or payment plans.
- Experience with causal machine-learning methods or treatment-effect modeling.
- Experience with supervised learning, model evaluation, and feature engineering, especially with large or complex datasets.
- Experience taking work from exploratory analysis and experimentation through production model development while maintaining analytical rigor and model reliability.
- Ability to translate broad servicing or business problems into structured research questions and communicate complex findings as actionable recommendations.
Benefits
- Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
- Retirement benefits including a 401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually
- Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees
- Comprehensive health coverage including medical, dental, vision, and wellness resources
- Health Savings Account contributions from Upstart for eligible plans
- Income protection benefits including life insurance and disability coverage
- Paid time off, sick leave, and company holidays
- Paid family and parental leave
- Family-centered benefits to support fertility, parenthood, and caregiving needs
- Employee Assistance Program (EAP) offering mental health support and life-centered resources
Workplace
- Digital-first workplace with flexibility to work remotely across the US and Canada (excluding Quebec).
- Onsite cadence varies, with most teams meeting once or twice per quarter for 2-4 consecutive days.
Skills
- Python
- Machine Learning
- Causal Inference
- Applied Statistics
- Supervised Learning
- Feature Engineering
- Probability
