below marketmost similar roles pay hereabove market
This role pays less than
67%
of similar roles. Most pay
$162,000–$238,162
— the blue band above.
At the midpoint, this role pays about
$169k
versus about
$200k
for comparable roles.
Based on 240 similar postings.
Employer
About Upstart
Upstart is an AI lending platform that partners with banks and credit unions to expand access to affordable credit using non-traditional variables.
Upstart currently has
54 open roles
on FindRole.
Listed pay typically runs
$166,900–$230,900
across 53 roles with salary data.
The Applied Scientist joins the Partnerships team to optimize customer offers across the journey. In this role, you will lead a greenfield optimization opportunity by moving from research and measurement to production-ready models and decision strategies. You will develop causal machine-learning models to determine which offers best support customer conversion, analyze complex data to identify gaps, and design statistically rigorous experiments to evaluate impacts. Key responsibilities include building production-quality code and translating research into actionable recommendations for technical and business partners. You will utilize Python for data manipulation and algorithm implementation, applying expertise in causal inference, supervised learning, feature engineering, and treatment-effect modeling. This work addresses the specific business problem of optimizing marketplace offers and pricing decisions to improve customer acquisition and conversion.
What does a Applied Scientist earn?
Median $208800 from 56 postings across 11 companies.
Develop causal machine-learning models to optimize customer offers across different stages of the marketing funnel.
Analyze complex data to identify knowledge gaps and design experiments to gather evidence for optimization models.
Measure the impact of offer changes using statistically rigorous experiments and evaluations.
Build production-ready models and code for use in automated offer selection.
Translate research findings into clear recommendations and decision strategies for business and technical partners.
Partner with leadership and engineering teams to align on strategy, implementation, and methodology.
What we're looking for
Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
PhD in a quantitative field (preferred).
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.
Expertise in applied statistics, probability, and experimental design to build or evaluate models or interventions.
Production-quality coding experience in Python, including data manipulation and algorithm implementation.
Experience with marketplace optimization, particularly where pricing or offer decisions are central (preferred).
Experience with supervised learning, model evaluation, and feature engineering with large or complex datasets (preferred).
Python
Machine Learning
Statistical Modeling
Supervised Learning
Feature Engineering
Model Evaluation
Probability
Statistics
Research
Quantitative Research
Python
Machine Learning
Causal Inference
Statistical Modeling
PySpark
Optimization
Experimental Design
Marketing Mix Modeling
Multi-Touch Attribution
Distributed Data Processing
Classification
Production ML Systems
Customer Lifetime Value
Next-Best-Action Systems
Python
Machine Learning
Causal Inference
Statistical Modeling
PySpark
Optimization
Experimental Design
Marketing Mix Modeling
Multi-touch Attribution
Classification
Distributed Data Processing
Production ML Systems
Customer Lifetime Value
Next-best-action systems