Principal Applied Scientist

Upstart Upstart · Fintech · Remote · Machine Learning

This role focuses on defining the long-term technical direction for Upstart's offer optimization and conversion modeling systems. The Principal Applied Scientist will work across teams to ensure models and optimization systems account for downstream effects, marketplace constraints, and customer outcomes. The work involves structuring ambiguous problem spaces, designing solutions for multi-stage customer journeys, and providing technical oversight. It sits at the intersection of operations research, optimization, causal machine learning, and production decision systems.

What you'd actually do

  1. Define the technical vision for how offer decisioning systems should interconnect across partnerships, always-on systems, and marketplace optimization
  2. Build and guide conversion modeling approaches that optimize decisions across multiple stages of the customer journey rather than in isolated local steps
  3. Ensure models and decision policies at one stage account for downstream impacts, business constraints, and later-stage optimization opportunities
  4. Design interfaces between decision systems and optimization or constraint-specification components
  5. Drive cross-functional technical alignment across teams that currently own adjacent pieces of the problem

Skills

Required

  • Advanced degree in a quantitative field such as statistics, mathematics, economics, computer science, operations research, or a related discipline
  • 8+ years of experience building and deploying machine learning models into production at scale
  • Experience with optimization, operations research, or constrained decision-making problems
  • Working knowledge of causal inference or causal machine learning
  • Strong grounding in statistics and probability
  • Experience leading large cross-functional technical initiatives with multiple stakeholders
  • Experience working across multiple technical teams to align approaches, define interfaces, and move toward a shared vision
  • Experience solving real-world machine learning or data science problems in a high-impact production environment

Nice to have

  • PhD in operations research, statistics, economics, computer science, or another quantitative field
  • Experience with offer optimization, pricing systems, marketplace optimization, or related decisioning systems
  • Experience with end-to-end modeling from problem framing through productionization
  • Experience in fintech, lending, marketplaces, or other domains where decisions must account for downstream constraints and business tradeoffs
  • Experience serving as a principal-level technical lead across multiple teams or product areas
  • Experience mentoring other senior scientists or raising the technical bar across an organization

What the JD emphasized

  • 8+ years of experience building and deploying machine learning models into production at scale
  • Experience leading large cross-functional technical initiatives with multiple stakeholders
  • Experience working across multiple technical teams to align approaches, define interfaces, and move toward a shared vision

Other signals

  • building decision systems
  • optimization
  • conversion modeling
  • marketplace dynamics
  • borrower behavior
  • unified technical vision
  • causal machine learning
  • production decision systems