We're looking for a Director of Data Science to own the strategy and technical leadership behind Mission Lane's credit acquisition models, reporting to the Sr. Director, Data Science.

The impact you'll make:

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.

What you'll own:

• 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

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 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.

Minimum qualifications:

• 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

Preferred qualifications:

• 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

Compensation:
Annual full-time starting base salary range: $184,000 - $219,000

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.