Senior Applied Scientist

Upstart

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

Posted 11/10/2026full-timeRemote • United States, CanadaSenior💰 $167,700 - $231,800 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing machine-learning models and applying causal inference methods to inform decision-making in servicing operations. Proficient in translating complex analytical findings into actionable strategies for product and operational enhancements.

Highest-signal resume keywords

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine-LearningCausal InferenceStatistical ModelingData ManipulationAlgorithm ImplementationExperimental DesignFeature EngineeringModel EvaluationQuantitative ResearchExploratory Analysis

Soft Skills

CommunicationCollaborationAnalytical Thinking

Certifications & Qualifications

Graduate Degree in Quantitative FieldPhD in Quantitative Field Preferred

Industry Keywords

Borrower Repayment BehaviorServicing OutcomesPayment ProductsCollections 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
2+ 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 preferred
Experience with causal machine-learning methods or treatment-effect modeling preferred
Experience with supervised learning, model evaluation, and feature engineering preferred
Experience taking work from exploratory analysis and experimentation through production model development 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
Supplemental health coverage for Canada
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
Fertility, parenthood, and caregiving benefits
Employee Assistance Program 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
Catered lunches and fully stocked micro-kitchens at offices