Senior Applied Scientist

Upstart

. Develop and evaluate machine-learning models that predict borrower repayment behavior and other servicing outcomes .

Posted 10/12/2026full-timeUnited StatesSenior💰 $167,700 - $231,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and evaluating machine-learning models, applying causal inference methods, and translating complex analytical findings into actionable business strategies. Proficient in Python for production-quality coding and experienced in collaborating with cross-functional teams to enhance payment experiences and servicing outcomes.

Highest-signal resume keywords

Machine-Learning Model DevelopmentCausal Inference MethodsProduction-Quality Python CodingApplied Statistics and Experimental DesignSupervised Learning and Model Evaluation

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine-LearningCausal InferenceStatistical ModelingApplied StatisticsExperimental DesignPython ProgrammingData ManipulationAlgorithm ImplementationFeature EngineeringModel Evaluation

Soft Skills

CommunicationCollaborationAnalytical Thinking

Certifications & Qualifications

PhD in Quantitative Field

Industry Keywords

Payment ProductsLoan ModificationsPayment AssistanceCollections StrategyAgent-Performance Insights

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & 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
Partner 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

What you’ll need

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
PhD in a quantitative field (preferred)
Experience with payment products or payment experiences, such as autopay, recurring payments, or payment plans (preferred)
Experience with causal machine-learning methods or treatment-effect modeling (preferred)
Experience with supervised learning, model evaluation, and feature engineering, especially with large or complex datasets (preferred)
Experience taking work from exploratory analysis and experimentation through production model development while maintaining analytical rigor and model reliability (preferred)
Ability to translate broad servicing or business problems into structured research questions and communicate complex findings as actionable recommendations (preferred)

Benefits

Comp & perks

Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
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 (US only)
Medical, dental, vision, and wellness resources
Health Savings Account contributions for eligible plans (US only)
Life insurance and disability coverage
Paid time off, sick leave, and company holidays
Paid family and parental leave
Family-centered fertility, parenthood, and caregiving benefits
Employee Assistance Program (EAP) with mental health support and life-centered resources
Financial planning tools and financial concierge service (US only)
Annual wellness allowance
Annual productivity allowance for relevant tools and resources
Team events, all-company updates, and employee resource groups (ERGs)
Catered lunches and fully stocked micro-kitchens at offices
Regular onsites and in-person meetings