Senior Machine Learning Manager, Upstart

• 6+ years of experience developing and deploying machine learning models in production with direct business impact
• Proven track record of leading high-impact ML initiatives from research through productionization
• Advanced degree in a quantitative field (e.g., computer science, statistics, economics, operations research, etc)
• Strong technical judgment and ability to dive deep into model design, data analysis, and evaluation
• Keen statistical, economic and business intuition, and comfort with reasoning under uncertainty and data limitations
• Knowledge of production ML, ability to adapt to new tech stacks and get in the weeds of work output from the team
• Excited and able to make direct technical contributions when needed
• Exceptional leadership skills with experience developing teams of highly technical ML scientists
• Attract, mentor, and grow a top-tier team of ML scientists passionate about expanding access to credit
• Proven ability to influence and collaborate cross-functionally with Product, Engineering, Capital Markets and other teams
• Strong project management skills with experience scaling processes and operational workflows

Desirable

• Our Core ML organization is looking for an exceptional, hands-on Machine Learning Manager to join our leadership group. Because our ML teams share common codebases and modeling pipelines, we are searching for generalist ML leaders who can be deployed to the areas of our business where they will have the most impact
• Rather than hiring for one specific silo, we match candidates to the right team based on their unique background, technical strengths, and interests. Depending on your expertise, you could step in to lead one of several high-priority teams, such as:
• Cash Line: Leading the 0→1 ML innovation for our brand new subscription-based line of credit, building core underwriting and customer behavior models (churn, draw, default) in a domain with limited data and long feedback loops
• Auto Retail Lending (ARL): Tackling unique, deep-modeling challenges such as competing risk, collateral, and recovery modeling for dealership-based auto lending
• Underwriting: Managing MLE-heavy engineering and research efforts to optimize our core unsecured personal loan models
• This is a highly technical player-coach role. You will not be managing a massive organization; instead, you will lead a small, nimble team of individual contributors (Research Scientists, Data Scientists, or Machine Learning Engineers)
• This role is designed for a builder who wants to retain meaningful strategic scope, maintain a roughly 50/50 split between technical execution and management, and act as the definitive ML owner for their product space
• Act as a Player-Coach: Dive deep into the data and code. You will spend a significant portion of your time making direct technical contributions, reviewing code/PRs, and understanding the mathematical nuances of your team's models
• Lead Strategic Initiatives: Take ownership of a specific product area (like Cash Line or Auto) and serve as the de facto ML leader in cross-functional strategy meetings
• Drive 0→1 and Scaling ML Efforts: Depending on your team placement, you may build out entirely new capabilities from scratch or optimize highly mature models dealing with massive scale and shifting macro-economic regimes
• Translate Models to Business Impact: Design and refine decision engines that translate model predictions into accurate, transparent, and customer-friendly lending outcomes