Upstart Research Scientist Interview Questions + Guide in 2025

Upstart is an innovative AI lending marketplace that partners with financial institutions to enhance access to affordable credit through advanced machine learning techniques.

As a Research Scientist at Upstart, you will play a pivotal role in developing and deploying cutting-edge machine learning models that assess borrower risk and optimize underwriting processes. Key responsibilities include researching new methodologies, prototyping models, collaborating with cross-functional teams, and evaluating model performance against business metrics. Success in this role requires a strong academic background in quantitative disciplines, proficiency in Python, and a solid understanding of statistical and machine learning principles. The ideal candidate should possess creative problem-solving skills, a sense of intellectual curiosity, and the ability to communicate complex technical concepts effectively. This role is critical to Upstart’s mission of enabling access to effortless credit based on true risk, and it demands a commitment to quality, collaboration, and continuous exploration of innovative solutions.

This guide will help you prepare effectively for your interview by providing insights into the skills and knowledge that are most relevant to the Research Scientist role at Upstart.

The interview process for a Research Scientist at Upstart is designed to assess both technical expertise and cultural fit within the organization. It typically consists of several stages, each focusing on different aspects of the candidate’s qualifications and alignment with Upstart’s mission.

1. Initial Recruiter Screen

The process begins with a phone interview with a recruiter, lasting about 30 minutes. This conversation serves as an introduction to the role and the company, allowing the recruiter to gauge your interest and fit for Upstart’s culture. Expect to discuss your background, motivations, and any relevant experiences that align with the responsibilities of a Research Scientist.

2. Technical Phone Interviews

Following the initial screen, candidates usually undergo two technical phone interviews. Each of these interviews focuses on assessing your proficiency in statistics, probability, and machine learning concepts. You may be asked to solve technical problems or discuss your previous projects in detail. Be prepared for questions that may reflect the interviewers’ specific interests, as they may have particular methodologies or solutions they prefer.

3. Coding Assessment

In some cases, candidates may be required to complete a coding assessment during the technical interviews. This could involve using a collaborative coding platform to solve problems related to data structures or algorithms. Familiarity with Python and the ability to articulate your thought process while coding will be crucial during this stage.

4. Onsite or Final Interview

The final stage typically involves an onsite interview or a series of video calls with team members. This round may include multiple one-on-one interviews, where you will be evaluated on your technical skills, problem-solving abilities, and how well you can communicate complex concepts to non-technical stakeholders. Expect to discuss your approach to model development, deployment, and evaluation, as well as your ability to work collaboratively within a team.

5. Cultural Fit Assessment

Throughout the interview process, Upstart places a strong emphasis on cultural fit. Candidates should be prepared to demonstrate their alignment with Upstart’s values, including intellectual curiosity, humility, and teamwork. Questions may focus on how you handle challenges, collaborate with others, and contribute to a positive team environment.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those related to your technical expertise and problem-solving skills.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Be prepared for a multi-stage interview process that typically includes phone screenings followed by technical interviews. The initial conversations may focus on your background and projects, but expect a significant emphasis on your technical skills, particularly in statistics and probability. Familiarize yourself with the specific areas of interest for the interviewers, as they may have idiosyncratic preferences in problem-solving approaches.

Master Probability and Statistics

Given the role’s focus on evaluating risk and developing predictive models, you should have a solid grasp of probability and statistics. Review key concepts such as mutual exclusivity, conditional probability, and statistical significance. Be ready to discuss how these concepts apply to real-world scenarios, particularly in the context of lending and credit evaluation.

Prepare for Technical Questions

Expect technical questions that may include coding challenges and theoretical inquiries related to machine learning and statistical modeling. Brush up on your Python skills, as proficiency in this language is crucial. Practice coding problems that involve data structures and algorithms, as well as statistical modeling techniques. Be prepared to explain your thought process clearly and concisely, as communication is key in technical interviews.

Communicate Effectively

During the interview, aim to communicate your ideas clearly and confidently. If you encounter a question that you find challenging, don’t hesitate to ask for clarification. This shows your willingness to engage and ensures that you understand the question fully. Additionally, practice explaining complex technical concepts in simple terms, as you may need to present your findings to non-technical stakeholders.

Showcase Your Curiosity and Teamwork

Upstart values intellectual curiosity and teamwork. Be prepared to discuss how you have collaborated with others in past projects and how you approach problem-solving. Share examples that highlight your ability to work in a team environment, your willingness to learn from others, and your drive to contribute to the team’s success.

Align with Upstart’s Mission

Demonstrate your enthusiasm for Upstart’s mission of expanding access to affordable credit. Familiarize yourself with the company’s values and how they align with your own. Be ready to discuss how your skills and experiences can contribute to Upstart’s goals, particularly in the context of developing innovative machine learning models that evaluate true risk.

Practice Mock Interviews

Consider conducting mock interviews with peers or mentors to simulate the interview experience. This will help you become more comfortable with the format and types of questions you may encounter. Focus on receiving constructive feedback to improve your responses and overall presentation.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Research Scientist role at Upstart. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Upstart. The interview process will likely focus on your understanding of machine learning, statistics, and your ability to apply these concepts to real-world problems, particularly in the context of lending and risk assessment. Be prepared to discuss your previous projects and how they relate to the role.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role, as it directly relates to model development.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the importance of labeled data in supervised learning and the exploratory nature of unsupervised learning.

Example

“Supervised learning involves training a model on a labeled dataset, where the outcome is known, such as predicting loan defaults based on historical data. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering borrowers based on their financial behaviors.”

2. How do you approach model selection for a new project?

This question assesses your understanding of the modeling process and your ability to make informed decisions.

How to Answer

Explain your criteria for model selection, including performance metrics, interpretability, and computational efficiency. Discuss how you would evaluate different models based on the specific problem at hand.

Example

“I start by defining the problem and the success metrics. Then, I explore various models, such as decision trees or neural networks, and evaluate them using cross-validation. I prioritize models that not only perform well but are also interpretable, especially in a lending context where stakeholders need to understand the decisions made by the model.”

3. Describe a machine learning project you worked on and the challenges you faced.

This question allows you to showcase your practical experience and problem-solving skills.

How to Answer

Detail a specific project, the challenges encountered, and how you overcame them. Focus on the impact of your work and any lessons learned.

Example

“In a project aimed at predicting borrower risk, I faced challenges with imbalanced data. I implemented techniques like SMOTE for oversampling the minority class and adjusted the model’s threshold to improve recall. This led to a significant increase in our model’s ability to identify high-risk applicants.”

4. What techniques do you use to prevent overfitting in your models?

This question tests your knowledge of model evaluation and validation techniques.

How to Answer

Discuss various strategies such as cross-validation, regularization, and pruning. Emphasize the importance of balancing model complexity with generalization.

Example

“To prevent overfitting, I use k-fold cross-validation to ensure that my model performs well on unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

5. How do you evaluate the performance of a machine learning model?

Understanding model evaluation is critical for ensuring the effectiveness of your solutions.

How to Answer

Explain the metrics you use for evaluation, such as accuracy, precision, recall, F1 score, and AUC-ROC. Discuss how these metrics apply to the lending context.

Example

“I evaluate model performance using metrics like precision and recall, especially in a lending context where false negatives can be costly. I also consider the AUC-ROC curve to assess the trade-off between true positive and false positive rates, ensuring that our model aligns with business objectives.”

Statistics & Probability

1. Can you explain the concept of p-values and their significance?

This question assesses your understanding of statistical inference.

How to Answer

Define p-values and explain their role in hypothesis testing. Discuss how they help in making decisions based on statistical evidence.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, which is crucial in determining the significance of our findings in model validation.”

2. What is the Central Limit Theorem and why is it important?

This question tests your foundational knowledge of statistics.

How to Answer

Explain the Central Limit Theorem and its implications for sampling distributions. Discuss its relevance in the context of model building and evaluation.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population’s distribution. This is important because it allows us to make inferences about population parameters even when the underlying data is not normally distributed.”

3. How do you handle missing data in your datasets?

This question evaluates your data preprocessing skills.

How to Answer

Discuss various strategies for handling missing data, such as imputation, deletion, or using algorithms that support missing values. Emphasize the importance of understanding the nature of the missing data.

Example

“I assess the extent and nature of the missing data first. If it’s missing at random, I might use mean or median imputation. For larger gaps, I consider more sophisticated methods like KNN imputation or even building models that can handle missing values directly, ensuring that we retain as much information as possible.”

4. Explain the concept of mutual exclusivity in probability.

This question tests your understanding of basic probability concepts.

How to Answer

Define mutual exclusivity and provide examples to illustrate the concept. Discuss its implications in decision-making processes.

Example

“Mutually exclusive events cannot occur simultaneously, such as a borrower either defaulting or not defaulting on a loan. Understanding this concept is crucial when calculating probabilities in risk assessment, as it affects how we model borrower behavior.”

5. How would you assess the correlation between two variables?

This question evaluates your ability to analyze relationships in data.

How to Answer

Discuss methods for assessing correlation, such as Pearson’s correlation coefficient, and the importance of visualizing data through scatter plots.

Example

“I would calculate Pearson’s correlation coefficient to quantify the strength and direction of the relationship between two variables. Additionally, I would visualize the data using scatter plots to identify any potential non-linear relationships that might not be captured by correlation alone.”