Senior Applied Scientist

Upstart; United States; Remote; USD 168,000 - 232,000; Full time

Job summary

Upstart is seeking a Senior Applied Scientist for its Servicing team to build predictive and causal models that improve loan servicing decisions. You will translate outputs into actionable rules affecting repayment, autopay, and borrower communications.

You’ll partner with Product, Engineering, and Operations to implement data-informed interventions, evaluate experiments, and scale models across the US remote work landscape.

Qualifications

• Experience applying causal inference methods and experimentation to decisions.
• Strong background in statistics, probability, and experimental design.
• Production-quality Python coding experience.
• 3+ years in ML, statistical modeling, or applied quantitative research.
• PhD preferred in a quantitative field.

Responsibilities

• Develop and evaluate machine-learning models that predict borrower repayment behavior and servicing outcomes.
• Apply causal inference and experimentation to measure impact of servicing interventions.
• Translate model predictions and treatment effects into practical decision algorithms.
• Partner with Product and Engineering on payment experiences, autopay, and borrower communications.
• Collaborate with Servicing and Collections Operations on strategy and insights.
• Identify ML opportunities in servicing areas historically optimized manually.

Skills

Causal inference expertise

Statistics / probability

Python programming

Machine learning / modeling

Experiment design

Communication of analytics

Education

Graduate degree in quantitative field

PhD in a quantitative field

Tools

Python

Job description

About the company

At the company, 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

the company’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.

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.

How you’ll make an impact

• 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.

Minimum Qualifications

• 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.

Position location

This role is available in the following locations: Remote — United States.

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.

At the company, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, the company uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

In addition, the company provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).

United States | Remote - Anticipated Base Salary Range

$167,700—$231,800 USD

What you'll love

At the company, our benefits for full-time employees are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect:

• Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly

• Retirement benefits to help you plan for the future, including a 401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually (USD in the US, CAD in Canada)

• Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees (US only)

• Comprehensive health coverage designed to support you and your family, including medical, dental, vision, and wellness resources for US and supplemental health coverage for Canada.

• Health Savings Account contributions from the company for eligible plans (US only)

• Income protection benefits, including life insurance and disability coverage for added financial security

• Paid time off, sick leave, and company holidays, in line with local requirements

• Paid family and parental leave to support caregiving and major life moments (duration varies by country)

• Family-centered benefits to support f