Lead ML Engineering Manager - Credit Risk

stripe.com

United States

Hybrid

USD 180,000 - 240,000

Full time

14 days+

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Benefits offered by this job

Hybrid/Remote work options

Job summary

Stripe seeks an Engineering Manager to lead a team of machine learning engineers focused on credit risk within its Risk & Financial Crimes organization. This role blends people leadership with technical direction, guiding ML initiatives that inform risk decisions and financial-crimes objectives across Stripe.

The role involves shaping the technical roadmap for ML systems, partnering with product, data science, and risk stakeholders, and overseeing the full lifecycle of production ML systems from

Qualifications

  • Experience leading ML engineering teams.
  • Applied ML background, ideally with exposure to credit risk, fraud detection, or financial crimes work.
  • Familiarity with production ML systems, including evaluation, monitoring, and model lifecycle management.

Responsibilities

  • Manage and develop a team of machine learning engineers working on credit risk modeling and decisioning
  • Shape the technical roadmap for ML systems that support risk and financial-crimes priorities
  • Partner with product, data science, and risk stakeholders to align on objectives and outcomes
  • Oversee the full lifecycle of production ML systems, from development through ongoing monitoring
  • Coach engineers on experimentation, model quality, and shipping reliable ML-powered products

Skills

ML leadership
Applied ML
Cross-functional collaboration

Job description

Stripe seeks an Engineering Manager to lead a team of machine learning engineers focused on credit risk within its Risk & Financial Crimes organization. This role blends people leadership with technical direction, guiding ML initiatives that inform risk decisions and financial-crimes objectives across Stripe.

The role involves shaping the technical roadmap for ML systems, partnering with product, data science, and risk stakeholders, and overseeing the full lifecycle of production ML systems from

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