Staff Applied Scientist

Upstart

3d•$199,500 - $276,000•Remote

About The Position

Upstart’s Machine Learning team advances the scientific methods and predictive systems that power its AI-driven lending marketplace. The team combines applied research, large-scale modeling, and cross-functional collaboration to improve decision quality, model reliability, and the effectiveness of the lending platform. As a Staff Applied Scientist, you will provide technical leadership across complex, high-impact machine learning initiatives. You will define scientific approaches to ambiguous problems, influence modeling strategy across teams, and establish methods that improve the performance and reliability of machine learning systems. The role is remote and includes periodic in-person collaboration through team onsites and meetings.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field, or equivalent practical experience.
At least 4 years of experience developing machine learning models or applying advanced statistical methods to complex quantitative problems.
Experience leading the technical design and implementation of machine learning solutions used in production environments.
Experience applying advanced machine learning methods and statistical evaluation techniques to large-scale datasets.
Experience defining technical approaches or architecture for machine learning initiatives involving multiple stakeholders or systems.

Nice To Haves

Advanced degree, ideally a PhD, in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
Knowledge of advanced machine learning techniques, including deep learning, ensemble methods, or probabilistic modeling.
Ability to evaluate complex tradeoffs involving predictive performance, interpretability, scalability, and model reliability.
Experience establishing modeling frameworks, evaluation standards, or reusable scientific tools adopted across teams.
Skills in influencing technical direction, mentoring scientists, and communicating scientific recommendations to senior stakeholders.

Responsibilities

Define technical direction for complex machine learning initiatives, translating broad business objectives into scientific strategies and measurable outcomes.
Lead the design and evaluation of advanced modeling approaches addressing predictive performance, robustness, and scalability.
Identify systemic opportunities to improve model development, experimentation, evaluation, and production performance across teams.
Partner with scientific, engineering, and business leaders to influence technical priorities and guide decisions involving complex modeling tradeoffs.
Establish scientific best practices, provide technical mentorship, and raise the quality and consistency of machine learning work across the organization.

Benefits

Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly.
401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually.
Employee Stock Purchase Plan with discounted stock purchase options for eligible U.S. employees.
Comprehensive medical, dental, and vision coverage and wellness resources; supplemental health coverage in Canada.
Health Savings Account contributions for eligible U.S. plans.
Life insurance and disability coverage.
Paid time off, sick leave, and company holidays in line with local requirements.
Paid family and parental leave; duration varies by country.
Family-centered benefits supporting fertility, parenthood, and caregiving.
Employee Assistance Program offering mental health support and life-centered resources.
Financial wellness resources, including financial planning tools and a financial concierge service for U.S. employees.
Annual wellness allowance supporting physical and emotional well-being and personal development.
Annual productivity allowance for relevant tools and resources.
Team events, all-company updates, and employee resource groups.
Catered lunches and stocked micro-kitchens when working from company offices.