About Upstart

Upstart is a leading AI lending marketplace partnering with banks and credit unions to expand access to affordable credit. By leveraging Upstart's AI marketplace, Upstart-powered banks and credit unions can have higher approval rates and lower loss rates across races, ages, and genders, while simultaneously delivering the exceptional digital-first lending experience their customers demand. More than two-thirds of Upstart loans are approved instantly and are fully automated.

Upstart is a digital-first company, which means that most Upstarters live and work anywhere in the United States. However, we also have offices in San Mateo, California; Columbus, Ohio; and Austin, Texas.

Most Upstarters join us because they connect with our mission of enabling access to effortless credit based on true risk. If you are energized by the impact you can make at Upstart, we’d love to hear from you!

The Team: 

Upstart aims to expand access to credit based on true risk.  As part of this mission, Upstart actively engages in marketing and borrower acquisition efforts to attract new customers. One key strategy involves direct mail (DM), an outbound marketing channel aimed at educating consumers about Upstart via physical mail and encouraging them to take out a loan. 

Upstart’s Machine Learning, Growth Direct Mail team develops advanced  DM models that predict conversion probabilities and effectively prioritize prospects. This team significantly contributes to Upstart's revenue and is integral to its overall success.

As a Research Scientist on this team, you will play a crucial role in advancing direct mail campaign effectiveness through cutting-edge machine learning techniques. Your responsibilities will include participating in research and development initiatives to innovate and fine-tune predictive models that accurately forecast conversion probabilities and prioritize prospects. Coordinate and run prospects selection for direct mail campaigns. Collaborating closely with cross-functional teams, you will design and implement experiments to optimize campaign outcomes and enhance ROI. Your role will encompass exploring novel algorithms and methodologies, assessing their impact on revenue metrics, and propose innovative strategies for leveraging machine learning to enhance Upstart’s marketing strategies.

How you’ll make an impact:

  • Participate in  research and development efforts to innovate and refine predictive models for direct mail campaigns, implementing state-of-the-art machine learning algorithms to enhance conversion probability accuracy.
  •  Coordinate and run prospects selection as part of direct mail campaigns.
  • Collaborate closely with cross-functional teams including engineers and marketing specialists to design and execute experiments aimed at optimizing campaign outcomes.
  • Evaluate the impact of model enhancements on revenue metrics and provide actionable insights to stakeholders.
  • Present findings and recommendations to stakeholders  and contribute to strategic discussions on enhancing campaign effectiveness and ROI.
  • Continuously propose and explore creative ways to leverage machine learning to improve marketing strategies and drive business growth.

Minimum Qualifications 

  • Advanced degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Proficiency in Python and in machine learning frameworks (e.g., XGBoost, PyTorch, sk-learn).
  • Strong understanding of statistical modeling,  experimental design and model development principles.

Preferred Qualifications:

  • 2-3 years of hands-on experience in applying machine learning techniques to real-world problems, preferably in marketing analytics or related fields.
  • Proven track record of developing and deploying predictive models using machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
  • Proficiency in Scala and Spark.
  • Strong critical thinking skills and a structured approach to problem-solving.
  • Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

What we’re looking for:

  • Strong academic credentials with a master's degree in statistics, mathematics, economics, computer science, or other quantitative areas of study; Ph.D. preferred
  • Programming skills in Python 
  • Full-stack expertise with all steps of the modeling process from ideation to productionalizing code; OR deep expertise in either statistical modeling or machine learning
  • Proficiency in a broad array of mathematical, statistical learning and machine learning concepts and applications
  • Knowledge of machine learning, pipelines and engineering architecture 
  • Detailed understanding of building good technical solutions; ability to convert ideas into testable hypotheses and/or next steps
  • Ability to meet team standards with respect to quality and velocity with minimal help or direction from peers
  • For more complex tasks, ability to break down problems into smaller tasks, and use them to estimate the timeline of executing a larger initiative. Willingness to make the right tradeoffs when needed to deliver projects on time.
  • Proactively communicate to stakeholders and teammates with ability to contextualize technical results 
  • Ability to mentor junior teammates with interest in technical and/or people leadership is a plus
  • Strong sense of intellectual curiosity balanced with humility, drive and teamwork
  • Numerically-savvy and smart with ability to operate at a speedy pace
  • Enthusiasm for and alignment with Upstart’s mission and values

Position location: This role is available in the following locations: Remote

Time zone requirements: The team operates on the East/West coast time zones.

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 but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.

What you’ll love: 

  • Competitive Compensation (base + bonus & equity)
  • Comprehensive medical, dental, and vision coverage
  • Personal Development and Technology & Ergonomic Budgets 
  • Life insurance and disability benefits 
  • Clubs and Activities  (Game Nights, Fitstarters, Superwomen, Book Club, Investing Club, Money Discussions, Photography Club and Basketball teams) 
  • Generous vacation policy
  • 401(k) and Employee Stock Purchase Plan (ESPP)
  • Catered lunches + snacks & drinks

 #LI-REMOTE

#LI-MidSenior

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
$123,200$170,500 USD

Upstart is a proud Equal Opportunity Employer. We are dedicated to ensuring that underrepresented classes receive better access to affordable credit, and are just as committed to embracing diversity and inclusion in our hiring practices. We celebrate all cultures, backgrounds, perspectives, and experiences, and know that we can only become better together. 

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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