below marketmost similar roles pay hereabove market
This role pays more than
69%
of similar roles. Most pay
$187,436–$246,000
— the blue band above.
At the midpoint, this role pays about
$238k
versus about
$217k
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 Staff Applied Scientist joins the Unsecured Underwriting Machine Learning team to develop and improve models informing credit decisions for unsecured lending products. In this role, you will research and implement enhancements to core underwriting models by designing, implementing, and evaluating model improvements through rigorous experimentation and validation methods. You will analyze model performance and downstream effects to ensure reliable decisioning while partnering with Engineering teams to support technical reviews and production deployment. The position requires a graduate degree in a quantitative field and experience with Python, probability, statistics, and machine learning methods. You will translate research findings into production-ready solutions, balancing research velocity with analytical rigor to solve complex problems regarding predictive performance and responsible oversight within the lending marketplace.
What does a Applied Scientist earn?
Median $208800 from 61 postings across 10 companies.
Research machine learning and statistical approaches to improve predictive performance of unsecured underwriting models.
Design, implement, and evaluate model enhancements using rigorous experimentation and validation methods.
Analyze model performance and downstream effects to ensure proposed changes improve decisioning reliably.
Partner with Engineering teams to support technical reviews, implementation, and deployment of models.
Translate research findings into clear recommendations, documented methodologies, and production-ready solutions.
What we're looking for
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 developing and evaluating machine learning or statistical models using Python.
Demonstrated knowledge of probability, statistics, and machine learning methods.
Experience designing experiments or validation analyses to assess model accuracy, reliability, or predictive performance.
PhD in a quantitative field (preferred).
Knowledge of supervised learning, model evaluation, and feature engineering techniques (preferred).
Experience working with large or complex datasets and translating research concepts into implemented solutions (preferred).
Large Language Models (LLMs)
Deep Learning
NLP
Reinforcement Learning
Fine-tuning
Model Optimization
Multimodality
A/B Testing
Data Curation
Predictive Analytics
Statistics
Computer Science
Python
Machine Learning
Statistical Modeling
Supervised Learning
Feature Engineering
Model Evaluation
Probability
Statistics
Research
Quantitative Research