Ondeck Data Scientist Interview Questions + Guide in 2025

Ondeck is a leading financial technology company that empowers small businesses with innovative financing solutions.

As a Data Scientist at Ondeck, you will play a crucial role in leveraging data to drive business decisions and enhance the customer experience. You will be responsible for developing predictive models, analyzing complex datasets, and delivering actionable insights that align with the company's mission to provide fast and flexible financing options. Key responsibilities include designing and implementing machine learning algorithms, conducting statistical analysis, and collaborating with cross-functional teams to inform product development and marketing strategies. The ideal candidate will possess strong programming skills in Python and SQL, a solid understanding of statistics and probability, and experience working with big data technologies. Additionally, a proactive attitude towards problem-solving and a passion for using data to drive business outcomes will set you apart as a great fit for this role at Ondeck.

This guide will help you prepare for your interview by providing insights into the expectations and skills that are valued by Ondeck, enabling you to showcase your qualifications effectively.

The interview process for a Data Scientist role at Ondeck is structured and involves multiple stages designed to assess both technical skills and cultural fit.

1. Initial Screening

The process begins with an initial screening, typically a 30-minute phone interview with a recruiter. This conversation focuses on your background, skills, and understanding of Ondeck's business model. The recruiter will gauge your fit for the company culture and may ask you about your knowledge of Ondeck and its operations.

2. Technical Assessment

Following the initial screening, candidates usually undergo a technical assessment. This may involve a coding test or a take-home project that evaluates your data manipulation skills, algorithmic thinking, and understanding of machine learning concepts. The technical assessment is crucial as it sets the stage for the subsequent interviews.

3. Phone Interviews

Candidates who successfully complete the technical assessment are invited to participate in one or two phone interviews. These interviews typically include discussions with a data scientist and possibly a hiring manager. Expect to tackle technical questions related to statistics, probability, and programming languages such as Python and SQL. You may also be asked to explain your previous projects and how they relate to the role.

4. Onsite Interview

The final stage of the interview process is an onsite interview, which can last around 4 to 5 hours. This stage consists of multiple rounds of interviews with team members, including technical and behavioral assessments. You will likely engage in whiteboarding exercises, where you will solve problems in real-time, and discuss your take-home project in detail. The onsite interviews are designed to evaluate your technical expertise, problem-solving abilities, and how well you collaborate with others.

Throughout the process, communication from the company is generally consistent, with updates provided on your application status.

As you prepare for your interview, it’s essential to be ready for the specific questions that may arise during these stages.

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

Understand OnDeck's Business Model

Before your interview, take the time to thoroughly research OnDeck's business model, products, and the specific challenges they face in the lending industry. Familiarize yourself with their data-driven approach to decision-making and how data science plays a crucial role in their operations. This knowledge will not only help you answer questions more effectively but also demonstrate your genuine interest in the company.

Prepare for Technical Assessments

Expect a variety of technical assessments throughout the interview process, including coding challenges and algorithm questions. Brush up on your Python and SQL skills, as these are frequently tested. Be prepared to solve problems related to data manipulation, statistical analysis, and machine learning algorithms, particularly logistic regression. Practicing whiteboarding exercises can also be beneficial, as many interviews involve collaborative problem-solving in this format.

Showcase Your Projects

During the interviews, you will likely be asked to discuss your previous projects in detail. Be ready to explain your thought process, the methodologies you used, and the impact of your work. Highlight any experience you have with big data problems, as this is particularly relevant to OnDeck's operations. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you convey the significance of your contributions.

Engage with Interviewers

While technical skills are crucial, OnDeck also values cultural fit and interpersonal skills. Approach your interviews as a two-way conversation. Ask thoughtful questions about the team dynamics, company culture, and how data science contributes to OnDeck's goals. This will not only help you gauge if the company is the right fit for you but also show your enthusiasm for the role.

Be Ready for Behavioral Questions

Expect behavioral questions that assess your problem-solving abilities and how you handle challenges. Prepare examples that demonstrate your resilience, teamwork, and adaptability. Given the mixed feedback about interviewers' engagement levels, be proactive in steering the conversation and showcasing your passion for data science.

Follow Up Professionally

After your interviews, send a thank-you email to express your appreciation for the opportunity and reiterate your interest in the position. This is also a chance to briefly mention any points you feel you may not have fully addressed during the interview. A thoughtful follow-up can leave a positive impression and keep you on the interviewers' radar.

By following these tips, you can present yourself as a well-rounded candidate who not only possesses the technical skills required for the Data Scientist role at OnDeck but also aligns with the company's values and culture. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at OnDeck. The interview process will likely assess your technical skills in data manipulation, machine learning, and statistical analysis, as well as your understanding of the company's business model and culture. Be prepared to discuss your past projects and how they relate to the role.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. Highlight scenarios where one might be preferred over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like customer segmentation in marketing data.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Discuss a specific project, focusing on the problem, your approach, and the results. Mention any obstacles and how you overcame them.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to balance the dataset, which improved our model's accuracy significantly.”

3. How would you handle missing data in a dataset?

This question tests your knowledge of data preprocessing techniques.

How to Answer

Explain various methods for handling missing data, such as imputation, deletion, or using algorithms that support missing values.

Example

“I would first analyze the extent and pattern of the missing data. If it’s minimal, I might use mean or median imputation. For larger gaps, I would consider using predictive modeling to estimate missing values or even dropping those records if they are not critical.”

4. What is cross-validation, and why is it important?

This question evaluates your understanding of model validation techniques.

How to Answer

Define cross-validation and explain its purpose in assessing model performance.

Example

“Cross-validation is a technique used to evaluate a model’s performance by partitioning the data into subsets. It helps ensure that the model generalizes well to unseen data, reducing the risk of overfitting.”

5. Can you explain logistic regression and its applications?

This question assesses your knowledge of statistical modeling.

How to Answer

Define logistic regression and discuss its use cases, particularly in binary classification problems.

Example

“Logistic regression is a statistical method for predicting binary outcomes based on one or more predictor variables. It’s widely used in scenarios like credit scoring, where we predict whether a loan will default or not.”

Statistics & Probability

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

This question tests your understanding of fundamental statistical concepts.

How to Answer

Explain the Central Limit Theorem and its implications for statistical inference.

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 crucial for hypothesis testing and confidence interval estimation.”

2. How do you interpret a p-value?

This question assesses your grasp of hypothesis testing.

How to Answer

Define p-value and explain its significance in statistical tests.

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, indicating a statistically significant result.”

3. Can you explain the concept of Type I and Type II errors?

This question evaluates your understanding of error types in hypothesis testing.

How to Answer

Define both types of errors and provide examples of each.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, a Type I error could mean falsely concluding that a new drug is effective when it is not.”

4. What is the difference between correlation and causation?

This question tests your ability to differentiate between two commonly confused concepts.

How to Answer

Clearly define both terms and provide examples to illustrate the difference.

Example

“Correlation indicates a relationship between two variables, while causation implies that one variable directly affects the other. For example, ice cream sales and drowning incidents are correlated, but eating ice cream does not cause drowning.”

5. How would you explain a confidence interval to a non-technical audience?

This question assesses your ability to communicate complex concepts simply.

How to Answer

Use simple language and relatable examples to explain confidence intervals.

Example

“A confidence interval gives us a range of values that likely contains the true population parameter. For instance, if we say we are 95% confident that the average height of a group is between 5’5” and 5’7”, it means that if we repeated the study many times, 95% of the intervals would capture the true average height.”