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# Senior Data Scientist, Credit

- **Company:** Mission Lane
- **Location:** United States
- **Experience:** Expert
- **Salary:** $120,000.0 - $134,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Command-Line Interface, Cloud Computing, Code Review, Databases, Python (Programming Language), Machine Learning, NumPy, Shell Script, Software Engineering, Supervised Learning, Google Cloud, Test-Driven Development (TDD), Scikit Learn, Kubernetes, Xgboost, Code Restructuring, Data Pipelines
- **Published:** November 3, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-data-scientist-credit--fff15175-a83c-49dd-8db4-f9c258a03f0d

## About the Role

Data Science, Data Science, Machine Learning, Credit Risk, Business Model, Software Engineering, Code Reviews, Refactoring, Test Driven Development (TDD), Regulations, * You're motivated by practical solutions, and curiosity is part of how you work every day.

 * You haven't necessarily worked in credit or lending before, but you're excited to learn a domain where regulations and long-horizon predictions matter.
 * You're comfortable with the full lifecycle of a model, including the less glamorous parts like data cleanup, monitoring, and validation.
 * You work well with people outside data science and can explain a modeling decision in plain language., * A degree in a quantitative field and 1+ years of work experience in a related role
 * Experience creating, deploying, and managing supervised learning models in a production system
 * Experience writing tested, reviewed, reproducible code, for data pipelines and model training alike, working fluently in the Python data stack.
 * Familiarity with working at the command line, shell scripting, databases, and cloud computing services
 * Ability to travel ~4+ times per year for high quality in-person collaboration

Preferred qualifications:

 * Experience solving problems in consumer lending or fintech
 * Experience with Airflow, Dagster, or other data pipeline platforms

## Description

* Design, build, and deploy supervised learning models that power Mission Lane's credit decisions, from first experiment through production
 * Partner with credit risk and portfolio teams to translate open business questions into modeling problems, and communicate the trade-offs between them
 * Monitor models already in production, catching drift and validating performance so they keep doing what they were built to do
 * Apply strong software engineering practices to your modeling work, including test-driven development, code review, and refactoring
 * Explore new data sources and modeling approaches using Mission Lane's Python data stack.

Our core tech stack includes:

Python and the Python data stack (numpy, polars, scikit-learn), LightGBM, DVC, Kubernetes, Airflow, Google Cloud, and Chalk, our feature store.