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Applied Scientist

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Remote from
USA, Canada
Salary
USD 141,500–196k / yr
Employment
Full Time
Experience
Open level
Published
Apply before
8 Nov 2026
Listing views
32
Application actions
1
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AI Summary

The role, at a glance.

Upstart is hiring an Applied Scientist for its Machine Learning Growth team to improve causal machine learning models used for borrower acquisition and marketing prospect selection. The role combines exploratory analysis, causal inference, experimental design, feature and model research, and production-ready model development. The scientist will evaluate historical campaign performance and expand modeling capabilities from direct mail into email, lifecycle, digital, HELOC, and other growth channels. Success requires strong Python-based data science skills, statistically rigorous experimentation, and the ability to translate business opportunities into actionable research and modeling plans.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a technically demanding applied ML role because it requires causal inference, rigorous experimentation, and end-to-end modeling on complex marketing datasets. The scientist must also connect research results to measurable business and campaign outcomes across cross-functional teams.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$168,750
US market range$135k–$210k
AI insightThe disclosed US remote anticipated base salary range is $141,500 to $196,000 USD yearly, with a midpoint of $168,750. This sits within a competitive US market range of approximately $135,000 to $210,000 annually for an applied scientist with causal inference, experimentation, and production machine-learning responsibilities. Bonuses and equity are mentioned but are not quantified and are excluded from the base-pay calculation.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you measure the incremental impact of a direct-mail prospecting model rather than simply its predictive accuracy?

I would design a randomized holdout or treatment-control experiment in which eligible prospects are assigned using the model-driven policy versus a baseline policy or no-contact control. I would estimate incremental conversion, revenue, cost, and confidence intervals, while checking treatment balance and accounting for exposure, attribution windows, and potential spillover effects.

Describe how you would approach a messy campaign dataset with inconsistent identifiers and incomplete outcome data.

I would first profile the data, document source systems, assess missingness, and define a canonical entity and event grain. I would create reproducible cleaning and join logic, implement data-quality checks, retain lineage for derived features, and validate final metrics against known campaign totals before using the data for modeling or causal analysis.

What is the difference between a model that predicts conversion and one that estimates uplift?

A conversion model estimates the likelihood that a person converts, regardless of whether they receive a treatment. An uplift model estimates the difference in expected outcomes between treatment and control, allowing the business to prioritize people whose behavior is likely to change because of the marketing intervention.

How would you decide whether a new model should be deployed to production?

I would evaluate offline performance, calibration, robustness across important segments, operational feasibility, and fairness or policy constraints. I would then recommend a controlled rollout with predefined success metrics and guardrails, compare it with the incumbent approach, and monitor performance, drift, and campaign-level incremental impact after deployment.

How do you communicate a complex causal-modeling result to Growth stakeholders?

I would begin with the decision and expected business impact, such as the estimated incremental conversions or cost efficiency improvement. I would explain assumptions and uncertainty in plain language, use clear visualizations and segment summaries, and provide a concrete recommendation along with the experiment or monitoring plan needed to validate it.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.

We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And 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, you’ll have the support to work in the way that works best for you.

If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from 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. The majority of our employees can live and work anywhere in the U.S or Canada (outside of Quebec) but are expected to spend high quality time in-person collaborating via regular onsites and in-person 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.

At Upstart, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, Upstart uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).

United States | Remote – Anticipated Base Salary Range

$141,500—$196,000 USD

At Upstart, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, Upstart uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).

Canada | Remote – Anticipated Base Salary Range

$130,900—$160,000 CAD

What you’ll love

At Upstart, our benefits for full-time employees are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect:

  • Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
  • Retirement benefits to help you plan for the future, including a 401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually (USD in the US, CAD in Canada)
  • Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees (US only)
  • Comprehensive health coverage designed to support you and your family, including medical, dental, vision, and wellness resources for US and supplemental health coverage for Canada.
  • Health Savings Account contributions from Upstart for eligible plans (US only)
  • Income protection benefits, including life insurance and disability coverage for added financial security
  • Paid time off, sick leave, and company holidays, in line with local requirements
  • Paid family and parental leave to support caregiving and major life moments (duration varies by country)
  • Family-centered benefits to support fertility, parenthood, and caregiving needs
  • Employee Assistance Program (EAP) offering mental health support and life-centered resources
  • Financial wellness resources, including access to financial planning tools and a financial concierge service (US Only)
  • Annual wellness allowance to support your physical and emotional well-being and personal development, based on what matters most to you
  • Annual productivity allowance to invest in relevant tools and resources you need to do your best work, no matter where you work from
  • Connection and community through team events, all-company updates, and employee resource groups (ERGs)
  • Onsite perks, including catered lunches and fully stocked micro-kitchens when working from one of our offices in the Bay Area, Austin, Columbus, and New York City

For roles based in Canada, please note that we are not currently able to hire in Quebec.

Upstart is a proud Equal Opportunity Employer. Just as we are dedicated to improving access to affordable credit for all, we are committed to inclusive and fair hiring practices.

If you require reasonable accommodation in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please email candidate_accommodations@upstart.com

https://www.upstart.com/candidate_privacy_policy

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This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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