Research Scientist

As a Research Scientist at Upstart, you will focus on researching and developing advanced machine learning models that enhance the accuracy of predictions in borrower risk, marketing conversions, and underwriting processes. Your role will involve designing, prototyping, and deploying these models into production while collaborating cross-functionally to optimize strategies and evaluate model performance against business metrics.

To qualify for the Research Scientist position at Upstart, you should have a Bachelor’s degree in a scientific or quantitative discipline and at least 2 years of experience in machine learning or a related field. Proficiency in programming languages like Python is essential, along with a strong grounding in statistical and machine learning theory. A Master's degree or PhD is preferred, alongside significant experience in building and deploying machine learning algorithms.

Upstart fosters collaboration by merging remote and in-person teamwork, offering regular on-site meetings and opportunities for cross-functional collaboration with engineers, product managers, and marketing specialists. This allows Research Scientists to engage with peers across different domains, ensuring that innovative ideas are explored and implemented effectively.

Upstart is an attractive company for Research Scientists primarily due to its commitment to leveraging AI technology to democratize access to credit. The opportunity to work on impactful projects with a focus on real-world applications of machine learning, coupled with a competitive compensation package and a dynamic company culture, makes it a great place for talented individuals looking to make a difference.

At Upstart, personal development is a priority, with budgets allocated for professional growth, technology upgrades, and ergonomic needs. Research Scientists will also have opportunities to mentor junior team members, enhancing their own skills while contributing to the team’s success. Engaging in clubs and activities tailored to interests further enriches the work environment.

When answering this question, highlight specific algorithms you've worked with and the context in which you applied them. Discuss your role in developing, testing, or deploying these algorithms, emphasizing your understanding of their strengths and weaknesses.

Describe your methodology for feature engineering, mentioning how you identify relevant features, handle missing data, and transform variables to improve model performance. Illustrate with examples from your past projects to provide context.

Explain the various metrics you use to evaluate model performance, such as accuracy, precision, recall, and F1 scores. Discuss the importance of selecting the right metrics based on specific business goals and provide examples from your experience.

Share your methods for staying informed about advancements in the field of machine learning, including following relevant journals, attending conferences, and participating in online courses. Emphasize how continuous learning enhances your work as a Research Scientist.

Provide an example of how you simplified technical jargon into easily understandable concepts while presenting to a non-technical audience. Highlight your ability to translate complex information into actionable insights that stakeholders can grasp.

Discuss a specific challenge in model deployment—perhaps issues with data consistency or integration with existing systems. Detail your approach to resolving the problem and any lessons learned that would apply to future projects.

Talk about your mentoring experience, including how you support junior team members in their professional development. Highlight specific strategies you use to guide them while fostering a collaborative and learning-oriented environment.

Describe your approach to prioritization, mentioning how you assess task urgency versus importance. Discuss tools or methodologies you use, such as Agile or Kanban, to manage your workload effectively.

Demonstrate your knowledge of Upstart's mission by discussing your enthusiasm for leveraging technology to increase access to credit. Explain how your skills align with this mission and what specific contributions you envision making as a Research Scientist.

Share your strategies for continuously improving model performance, such as A/B testing, gathering user feedback, and performing regular audits. Talk about the importance of an iterative approach in refining models over time.