Senior Machine Learning Manager

Role Overview

Join Upstart’s Core ML organization as a Senior Machine Learning Manager. This is a hands-on player-coach leadership role where you lead a small team and drive strategic ML initiatives tied to lending products.

Key Missions

• Player-Coach leadership:
  • Dive into data and code and make direct technical contributions
  • Review code/PRs and support teams with the mathematical nuance of models
• Own strategic product areas:
  • Take de facto ML leadership for a specific area (e.g., Cash Line, Auto Retail Lending, Underwriting)
  • Participate in cross-functional strategy meetings and align with stakeholders
• 0→1 and scaling ML efforts:
  • Depending on placement, either build new capabilities from scratch or optimize mature models
  • Operate at massive scale and adapt to changing macro-economic regimes

Responsibilities

• Lead and manage a team of ML scientists (hiring/mentoring/growth)
• Drive end-to-end ML initiatives from research through production
• Ensure model design, data analysis, and evaluation are rigorous
• Collaborate cross-functionally with Product, Engineering, Capital Markets, and others
• Uplevel engineering practices and participate in cross-functional engineering design

Requirements

• 6+ years building and deploying machine learning models in production with business impact
• Proven track record leading high-impact ML initiatives from research → productionization
• Strong production ML knowledge with ability to get in the weeds of team outputs
• Advanced quantitative degree (e.g., Computer Science, Statistics, Economics, Operations Research) and/or PhD in a related field
• Strong statistical, economic, and business intuition; comfort with uncertainty and data limitations
• Hands-on familiarity with end-to-end ML infrastructure, including:
  • Experimentation pipelines
  • Feature stores
  • Model monitoring
• Familiarity with lending/lines of credit/consumer finance products
• Strong project management skills, including scaling processes and operational workflows

Nice-to-Haves

• Proven success building and scaling new ML products from inception
• Deep experience optimizing mature models under large-scale, shifting economic conditions