Job Title: Applied Scientist

Source: weworkremotely
Location: United States | Remote

About Upstart

At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.

As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers.

We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. Whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.

The Team

Upstart’s Machine Learning Growth team develops models that help optimize borrower acquisition across marketing channels. The Direct Mail team focuses on causal machine learning models that predict incremental conversion and help prioritize prospects, and its scope is expanding beyond Personal Loans into Home Equity Line of Credit (HELOC), email marketing, digital, and other marketing use cases.

The Role

As an Applied Scientist at Upstart, you will improve existing models and develop new approaches that expand the team’s impact across marketing channels. You will work across business-oriented analysis, machine learning research, experimentation, and production-ready modeling, partnering primarily within Machine Learning and with Growth and Marketing Platform Engineering stakeholders.

How You’ll Make an Impact

Analyze historical model and campaign performance to identify opportunities to improve model effectiveness and marketing outcomes.
Develop and evaluate machine learning models, including researching new features and model architectures, to improve prospect selection and support new marketing use cases.
Design statistically rigorous experiments and model evaluations to measure causal impact, optimize campaign outcomes, and inform decisions.
Navigate complex, messy datasets and build reusable data pipelines, metrics, and analytical approaches that enable efficient model development and analysis.
Partner with Machine Learning, Growth, and Marketing Platform Engineering teams to translate business problems into research agendas, intermediate milestones, and production-ready solutions.
Expand machine learning capabilities beyond direct mail into lifecycle, email, digital, and other emerging marketing channels.

Minimum Qualifications

Master’s Degree in Mathematics, Statistics, Economics, Operations Research or a related field.
Experience applying statistical and machine learning methods to modeling or data science problems.
Experience using Python for data analysis, data preparation, and machine learning model development.
Experience with causal inference and experimental design, including statistically rigorous evaluation of model or experiment performance.

Preferred Qualifications

PhD in Mathematics, Statistics, Economics, Operations Research or a related field (or its equivalent).
Knowledge of causal machine learning methods and modeling approaches.
Ability to translate broadly scoped business problems into structured research questions, analyses, and modeling approaches.
Experience working across exploratory data analysis, machine learning research, experimentation, and production model development and scaling.
Ability to interpret complex experimental or modeling results and translate findings into actionable recommendations.
Experience applying machine learning to marketing, customer acquisition, lifecycle, or other growth use cases.

Travel Requirements

As a digital-first company, the majority of your work can be accomplished remotely. Employees are expected to spend high-quality time in-person collaborating via regular onsites and meetings. The onsite cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

Compensation

Upstart provides employees with target bonuses, equity compensation, and generous benefits packages.

United States | Remote

Anticipated Base Salary Range: $141,500 – $196,000 USD

Canada | Remote

Anticipated Base Salary Range: $130,900 – $160,000 CAD
Note: We are not currently able to hire in Quebec.

What You’ll Love

Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly.
Retirement benefits with a company match of $2 for every $1 contributed, up to $15,000 annually.
Employee Stock Purchase Plan (ESPP) with discounted stock purchase options (US only).
Comprehensive health coverage (medical, dental, vision) and wellness resources.
Health Savings Account contributions (US only).
Income protection benefits, including life insurance and disability coverage.
Paid time off, sick leave, and company holidays.
Paid family and parental leave.
Family-centered benefits to support fertility, parenthood, and caregiving.
Employee Assistance Program (EAP) for mental health support.
Financial wellness resources and concierge service (US only).
Annual wellness and productivity allowances.
Connection and community through team events and employee resource groups (ERGs).
Onsite perks when working from our offices in the Bay Area, Austin, Columbus, and New York City.

Equal Opportunity

Upstart is a proud Equal Opportunity Employer. If you require reasonable accommodation in the application or interview process, please email: candidate_accommodations@upstart.com

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