Senior Manager, Machine Learning

About the role

You will own the end-to-end lifecycle of high-impact machine learning initiatives that directly shape lending decisions and customer outcomes at Upstart. You will act as the technical leader for your assigned product area, balancing hands-on modeling work with people management and strategic influence. This role requires you to translate ambiguous business problems into well-defined ML solutions while ensuring rigorous validation and compliance. You will partner closely with product, risk, and engineering stakeholders to integrate advanced modeling into production systems. Your work will directly affect the cost and quality of credit available to millions of Americans. If you thrive in fast-moving, data-rich environments and care deeply about responsible AI, this is your chance to build systems that matter.

Key facts

What you'll do

• Lead the discovery and scoping of new machine learning initiatives, defining success metrics and validation frameworks before any modeling begins.
• Design and iterate on experimental methodologies, such as holdout strategies and causal inference approaches, to ensure robust performance measurement in dynamic economic conditions.
• Partner with product and risk teams to translate business objectives into precise modeling targets, balancing lift, fairness, and regulatory considerations.
• Review and mentor code contributions, data pipelines, and model artifacts, maintaining high standards for reproducibility, documentation, and testing.
• Own the technical roadmap for your product area, prioritizing experiments and model improvements based on impact, feasibility, and risk.
• Collaborate with data engineering to define feature store requirements, ensuring that features remain consistent, interpretable, and scalable across teams.
• Evaluate and select modeling architectures, including tree-based ensembles and neural approaches, while monitoring for degradation in real-world performance.
• Communicate model behavior and limitations to non-technical stakeholders, aligning expectations and building trust across the organization.
• Drive the deployment of models into production, coordinating with infrastructure and platform teams to ensure reliable, low-latency serving.
• Track business outcomes rigorously, using analytics to link model behavior to financial performance and customer wellbeing.
• Identify and mitigate potential bias, ensuring that modeling practices align with our commitment to fair and inclusive lending.
• Lead the technical due diligence of third-party models or vendors, assessing suitability for integration into our core lending systems.
• Define guardrails and monitoring dashboards to detect data drift, concept drift, and anomalies in model inputs or outputs.
• Serve as the go-to expert for machine learning strategy within your product area, influencing long-term priorities and investment decisions.

Requirements

You must meet the following minimum qualifications, as outlined in our official job criteria.

• Bachelor's degree in a quantitative field or equivalent practical experience.
• 6+ years of relevant experience in machine learning, data science, or a closely related discipline.
• At least 3 years of hands-on experience building and deploying machine learning models in production environments.
• Demonstrated expertise in at least one programming language commonly used for data science and machine learning, such as Python or R.
• Strong foundation in statistical learning, including regression, classification, and evaluation techniques.
• Experience working with large datasets and distributed computing frameworks where appropriate.
• Familiarity with software engineering best practices, including version control, testing, and code review.
• Clear written and verbal communication skills for collaborating with both technical and non-technical audiences.

Nice to have

Experience in lending, financial services, or other heavily regulated industries is preferred. Background in causal inference, uplift modeling, or experimental design is also valued. Experience leading small technical teams or mentoring junior data scientists is considered a plus.

Practical notes

This is a full-time position based in the United States. Remote work is available, with expectations around collaboration during core business hours. Travel is not required for this role. No specific visa sponsorship details are provided in this source. The engagement is full-time, and the role reports to the Core ML leadership group.