Director of Data Science, Credit

Someone hits submit on a credit card application, hopeful this is the “yes” that lets them start building toward something: a car, a home, a little more room to breathe. You'll lead the strategy and the team behind decisions like that one. Keeping every decision accurate and fair as circumstances shift makes this work interesting and rewarding, every day.

Mission Lane is young, but we’ve landed in an exciting, stable stretch of maturation: still moving toward what we’re going to become, with plenty of room for you to help shape it.

• Mission Lane's credit acquisition modeling strategy, translating the company's growth and underwriting goals into a roadmap that balances approval rates, portfolio performance, and fair lending practice
• Technical leadership for the data scientists building and maintaining acquisition models, setting the bar for model design, code quality, and production rigor
• Model risk and monitoring practices that keep acquisition models compliant, explainable, and accurate as underwriting conditions change
• Cross-functional alignment with Credit Risk, Portfolio, Data Engineering, and company leadership on how acquisition modeling fits into Mission Lane's broader risk appetite
• A growing scope as acquisitions-adjacent growth initiatives roll into the team, giving you room to shape how the function expands

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

• You stay anchored to the business problem you're solving, keeping the modeling technique in service of the goal.
• You're curious by nature, the kind of person who wants to understand how the pieces fit together.
• You've made predictions where the outcome doesn't show up for a year or more, and you build in the discipline that requires, including practicing sound model risk management.
• You can mentor and raise the technical bar for experienced data scientists without needing to be the smartest person in every room.
• You partner naturally with people outside data science, translating technical trade-offs into decisions the business can act on.

• Has a PhD in a quantitative field and 5+ years of experience in a related role, or a BS/MS in a quantitative field and 8+ years of experience in a related role
• Has created, deployed, and managed supervised learning models in a production environment for high-impact applications
• Has direct experience hiring, coaching, and developing data scientists as their manager
• Has applied data science to credit risk, underwriting, or another long-horizon, regulated prediction problem, such as insurance
• Writes tested, reviewed, reproducible code, for data pipelines and model training alike, and works fluently in the Python data stack.
• Able to travel ~4-6+ times per year for high quality in-person collaboration

• Direct experience in consumer lending; credit card acquisitions, specifically
• Familiarity with Mission Lane's broader ML tooling ecosystem, including Chalk, BentoML, or DVC
• Experience partnering directly with executive leadership on modeling strategy

Mission Lane is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, or any other protected status. 

Mission Lane provides reasonable accommodations to applicants who need them for medical or religious reasons, as required by law.  Applicants can initiate an accommodation request by contacting peopleexperience@missionlane.com.

Mission Lane is not sponsoring new applicant employment authorization and please, no third-party recruiters.

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.}