Senior Data Scientist, Credit

The impact you'll make

Behind every "yes" from Mission Lane is a model that had to see past a credit score to the story behind it, the kind of story that doesn't fit neatly into three digits. You'll build and sharpen the models that read that signal right: accurate enough to trust, fair enough to stand behind, and sound enough to move our customers forward.

• A lot of our applicants don't have years of clean credit history to lean on, so the standard score alone doesn't get you very far. That means you'll get to dig into the interesting work of figuring out what else predicts repayment.
• Mission Lane is still young, but we've landed in an exciting, stable stretch of maturation, and you'll learn and grow right along with us.

As Senior Data Scientist you will

• 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.

You'll thrive in this role if

• 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.

Minimum qualifications

• 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

Compensation

Annual full-time starting base salary range: $120,000 - $134,000

This role is eligible for additional compensation in the forms of participation in our annual incentive and equity programs.

Pay is based on factors such as work experience, education, certification(s), training, skills, and competencies related to the role. Mission Lane also offers a comprehensive benefits plan, which includes paid time off, 401(k) match, a monthly wellness stipend, health/dental/vision insurance options, disability coverage, paid parental leave, flexible spending account (for childcare and healthcare), life insurance, and a remote-first work environment.

Application Integrity

Our cardholders trust us with their financial well-being, and this trust starts with the integrity of the people on our team. We're looking for team members who share our dedication to transparency and truth. Please verify that the information in your application is accurate and complete.

Providing any information to Mission Lane that is not completely truthful at any point during the application or hiring process may result in removal from the hiring process, disqualification from future opportunities, withdrawal of an offer or other sanctions for candidates and, in addition for employees, disciplinary action, up to and including termination of employment.