Mission Lane

Mission Lane

Director of Data Science, Credit

Remote, United States · Director · Full-time

Sponsorship not specified$184k-$219kDetected 10 days ago
PythonGCPKubernetesMachine Learningscikit-learnNumPyAirflowData EngineeringData ScienceLeadershipCollaborationMentoringUnderwriting

Stay score

odds of building a lasting career here

59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

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About the role

  • By attracting top talent and leveraging cutting-edge technology, we're enabling people to unlock real financial progress.
  • Keeping every decision accurate and fair as circumstances shift makes this work interesting and rewarding, every day.
  • Pay is based on factors such as work experience, education, certification(s), training, skills, and competencies related to the role.

Responsibilities

  • 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 partner naturally with people outside data science, translating technical trade-offs into decisions the business can act on.
  • By fostering a culture of empathy and collaboration, we create a strong sense of belonging and support for every team member.

Requirements

  • 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
  • You can mentor and raise the technical bar for experienced data scientists without needing to be the smartest person in every room.

Nice to have

  • 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
  • About Mission Lane:
  • It all started with a realization: nearly fifty percent of the adult population in the U.S. doesn't have access to a clear line of credit.
  • Most traditional credit card companies either overlook or overcharge this group because they have less-than-perfect credit scores or no scores at all.
  • We decided this just wouldn't do.

Skills

  • Model risk and monitoring practices that keep acquisition models compliant, explainable, and accurate as underwriting conditions change
  • A growing scope as acquisitions-adjacent growth initiatives roll into the team, giving you room to shape how the function expands

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.

Company info

  • About Mission Lane: Founded in December 2018, Mission Lane is a purpose-driven fintech company based in the U.S., with headquarters in Richmond, Virginia.
  • We're looking for team members who share our dedication to transparency and truth.
  • Founded in December 2018, Mission Lane is a purpose-driven fintech company based in the U.S., with headquarters in Richmond, Virginia.

Equal opportunity

  • Mission Lane is an equal opportunity employer.
  • Applicants can initiate an accommodation request by contacting peopleexperience@missionlane.com.
  • equal opportunity employer.

Visa & Work Authorization

  • In partnership with our sponsor banks, we offer credit cards under the Mission Lane brand name, with better, clearer terms, and a more refined customer experience than the alternatives available to people workin

This listing is sourced directly from Mission Lane's careers page and normalized into a canonical job model.