Applied Scientist

Upstart

. Develop causal machine-learning models to optimize customer offers across the funnel .

Posted 10/28/2026full-timeRemote • United StatesJuniorMid-Level💰 $141,500 - $196,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in causal machine-learning model development and applied statistics, with a strong focus on experimental design and data analysis. Proficient in Python for production-quality coding and model evaluation, with experience in marketplace optimization and decision-making strategies.

Highest-signal resume keywords

Causal Machine-LearningApplied StatisticsExperimental DesignPython ProgrammingMarketplace Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Causal Inference MethodsStatistical ModelingData AnalysisModel EvaluationFeature EngineeringTreatment-Effect ModelingSupervised LearningProduction-Model DevelopmentAlgorithm ImplementationData Manipulation

Certifications & Qualifications

Graduate Degree in Quantitative FieldPhD in Quantitative Field (Preferred)

Industry Keywords

Machine LearningApplied ScienceData ScienceQuantitative ResearchDecision Strategies

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Develop causal machine-learning models to optimize customer offers across the funnel
Analyze complex data, identify knowledge gaps, and design experiments to gather evidence
Measure the impact of offer changes using statistically rigorous experiments and evaluations
Translate research and results into recommendations, decision strategies, and next steps
Build production-ready models and code for offer selection
Partner with Partnerships leadership, Product, Engineering, peer machine-learning teams, and model-risk partners on strategy, implementation, and methodology

Requirements

What you’ll need

Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field
2+ years of experience conducting machine-learning, statistical-modeling, or applied quantitative research in an academic or industry setting
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
PhD in a quantitative field (preferred)
Experience with marketplace optimization, causal machine-learning methods, treatment-effect modeling, supervised learning, model evaluation, feature engineering, and production-model development (preferred qualifications)

Benefits

Comp & perks

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 (US only)
Comprehensive health coverage including medical, dental, vision, and wellness resources for US employees
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
Family-centered benefits supporting fertility, parenthood, and caregiving
Employee Assistance Program offering mental health support and life-centered resources
Financial planning tools and financial concierge service (US only)
Annual wellness allowance
Annual productivity allowance for relevant work tools and resources
Team events, all-company updates, and employee resource groups (ERGs)
Catered lunches and stocked micro-kitchens at offices in the Bay Area, Austin, Columbus, and New York City