Step Data Scientist Interview Questions + Guide in 2025

Step is a next-generation financial services company that aims to empower teens and young adults to achieve financial independence through an innovative, mobile-first banking experience.

As a Data Scientist at Step, you will play a pivotal role in building and deploying machine learning models focused on enhancing Risk and Fraud detection systems. Your responsibilities will encompass designing algorithms that protect the company and its customers from financial losses, leading technical efforts in the Risk/Fraud domain, and ensuring that data manipulation and code development align with best practices. You will collaborate cross-functionally, leveraging your statistical expertise to guide experiments and evaluate model performance effectively. To excel in this role, a strong foundation in statistics, proficient coding skills in Python and SQL, and the ability to communicate complex concepts to both technical and non-technical audiences are essential. Experience in financial systems, while beneficial, is not a strict requirement.

This guide will help you prepare for the interview by providing insight into the expectations for the role and the skills that will be assessed, giving you a competitive edge in showcasing your qualifications and fit for Step.

The interview process for a Data Scientist role at Step is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the company's mission and culture. The process typically includes several key stages:

1. Initial Screening

The process begins with an initial screening conducted by a recruiter. This 30-minute phone interview focuses on understanding your background, experience, and motivation for applying to Step. The recruiter will also assess your fit for the company culture and discuss the role's expectations.

2. Technical Interview

Following the initial screening, candidates will participate in a technical interview. This round is designed to evaluate your proficiency in data science concepts, particularly in statistics, machine learning, and coding. Expect to solve problems related to data manipulation using SQL and Python, as well as demonstrate your understanding of algorithms and data structures. You may encounter questions that require you to apply statistical methods to real-world scenarios, such as designing experiments or analyzing datasets.

3. Case Study or Practical Assessment

Candidates may be asked to complete a case study or practical assessment, where you will analyze a dataset and present your findings. This exercise tests your analytical skills and ability to derive actionable insights from data. You should be prepared to discuss your approach, the methodologies you used, and the rationale behind your decisions.

4. Behavioral Interview

The behavioral interview focuses on assessing your soft skills and how you align with Step's values. Interviewers will ask about your past experiences, how you handle challenges, and your ability to work collaboratively in a team. Be ready to provide examples that showcase your problem-solving abilities and communication skills, particularly in cross-functional settings.

5. Final Interview

The final interview may involve meeting with senior leadership or team members to discuss your fit within the team and the company. This round often includes a mix of technical and behavioral questions, as well as discussions about your long-term career goals and how they align with Step's mission.

As you prepare for the interview process, it's essential to familiarize yourself with the types of questions that may be asked, particularly those related to your technical expertise and past experiences.

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

Understand the Role's Technical Requirements

As a Data Scientist at Step, you will be expected to have a strong grasp of statistics, probability, and algorithms, as well as proficiency in SQL and Python. Make sure to review key concepts in these areas, especially focusing on statistical methods that guide experiments and model evaluation. Brush up on your knowledge of machine learning algorithms, as you may be asked to explain the differences between techniques like boosting and bagging, or to discuss how you would approach a specific problem in risk and fraud detection.

Prepare for Data Structures and Algorithms Questions

Expect to face technical questions that assess your problem-solving skills, particularly in data structures and algorithms. Practice medium-level LeetCode questions that involve hashmaps, strings, and linked lists, as these are commonly tested areas. Familiarize yourself with graph and tree data structures, as well as their applications in real-world scenarios. Being able to articulate your thought process while solving these problems will be crucial, so practice coding in a collaborative environment, such as Google Docs, to simulate the interview experience.

Showcase Your Experience with Data Manipulation

You will likely be asked about your experience with data manipulation and analysis. Be prepared to discuss specific projects where you used SQL to fetch, transform, and prepare data for model development. Highlight your ability to communicate complex technical concepts to non-technical stakeholders, as this is a key skill for the role. Consider preparing a case study or example that illustrates your analytical skills and how you derived insights from data.

Emphasize Cross-Functional Collaboration

Step values teamwork and cross-functional partnerships, especially in rapidly evolving environments. Be ready to discuss how you have collaborated with other teams in previous roles, particularly in situations where you had to respond to changing business needs. Share examples that demonstrate your ability to work effectively with operations or product teams, and how your contributions led to successful outcomes.

Prepare for Behavioral Questions

In addition to technical assessments, expect behavioral questions that explore your motivations, strengths, and weaknesses. Reflect on your past experiences and be ready to discuss how they align with Step's mission and values. Prepare to answer questions about challenges you've faced, how you handle stress, and what drives you in your work. Authenticity and self-awareness will resonate well with interviewers.

Familiarize Yourself with Company Culture

Understanding Step's mission to empower the next generation financially will help you connect your personal values with the company's goals. Research the company culture and be prepared to discuss why you want to work at Step specifically. Show enthusiasm for their innovative approach to banking and how you can contribute to their mission of improving financial literacy among teens and young adults.

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

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Step. The interview process will likely focus on your technical skills in data science, machine learning, and statistical analysis, as well as your ability to communicate complex concepts clearly. Be prepared to demonstrate your problem-solving abilities and your experience with data manipulation and model deployment.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial. Be clear about the definitions and provide examples of each type.

How to Answer

Discuss the key characteristics of both supervised and unsupervised learning, emphasizing the role of labeled data in supervised learning and the absence of labels in unsupervised learning.

Example

“Supervised learning involves training a model on a labeled dataset, where the input data is paired with the correct output. For instance, predicting house prices based on features like size and location is a supervised task. In contrast, unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings, such as clustering customers based on purchasing behavior.”

2. What techniques would you use to handle imbalanced datasets?

This question assesses your knowledge of practical machine learning challenges.

How to Answer

Mention techniques such as resampling methods, using different evaluation metrics, or applying algorithms that are robust to class imbalance.

Example

“To address imbalanced datasets, I would consider techniques like oversampling the minority class or undersampling the majority class. Additionally, I might use evaluation metrics like F1-score or AUC-ROC instead of accuracy to better assess model performance.”

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

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using metrics like accuracy for balanced datasets, but I prefer precision and recall for imbalanced datasets. The F1-score provides a balance between precision and recall, while ROC-AUC gives insight into the model's ability to distinguish between classes.”

4. Can you describe a machine learning project you worked on? What challenges did you face?

This question allows you to showcase your practical experience.

How to Answer

Outline the project, your role, the challenges encountered, and how you overcame them.

Example

“In a recent project, I developed a fraud detection model. One challenge was dealing with an imbalanced dataset. I implemented SMOTE to generate synthetic samples for the minority class, which improved the model's performance significantly.”

Statistics & Probability

1. Explain the concept of p-values in hypothesis testing.

This question assesses your understanding of statistical significance.

How to Answer

Define p-values and explain their role in hypothesis testing.

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 that the observed effect is statistically significant.”

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

This question tests your grasp of fundamental statistical concepts.

How to Answer

Explain the theorem and its implications for sampling distributions.

Example

“The Central Limit Theorem states that the distribution of the sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial because it allows us to make inferences about population parameters using sample statistics.”

3. How would you determine the sample size for an experiment?

This question evaluates your ability to design experiments.

How to Answer

Discuss factors that influence sample size, such as effect size, power, and significance level.

Example

“To determine sample size, I would consider the expected effect size, the desired power of the test (commonly 0.8), and the significance level (usually 0.05). Using these parameters, I can apply power analysis to calculate the necessary sample size.”

4. Can you explain the difference between Type I and Type II errors?

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

How to Answer

Define both types of errors and provide examples.

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 concluding that a new drug is effective when it is not, whereas a Type II error would mean failing to detect that the drug is effective when it actually is.”

Data Manipulation

1. How do you handle missing data in a dataset?

This question tests your data cleaning skills.

How to Answer

Discuss various strategies for dealing with missing data, such as imputation or removal.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. Depending on the situation, I might use imputation techniques, like filling in missing values with the mean or median, or I may choose to remove rows or columns with excessive missing data.”

2. Can you describe how you would use SQL to extract data for analysis?

This question evaluates your SQL skills.

How to Answer

Explain your approach to writing SQL queries for data extraction.

Example

“I would use SQL to write queries that join multiple tables to gather relevant data. For instance, I might use a SELECT statement with JOIN clauses to combine user data with transaction data, filtering results based on specific criteria to prepare for analysis.”

3. What is data wrangling, and why is it important?

This question assesses your understanding of data preparation.

How to Answer

Define data wrangling and its significance in the data analysis process.

Example

“Data wrangling is the process of cleaning and transforming raw data into a usable format. It’s crucial because high-quality data is essential for accurate analysis and model performance. Poorly prepared data can lead to misleading results.”

4. How do you ensure data quality in your analyses?

This question evaluates your approach to maintaining data integrity.

How to Answer

Discuss methods you use to validate and clean data.

Example

“I ensure data quality by implementing validation checks during data collection, performing exploratory data analysis to identify anomalies, and using automated scripts to clean and standardize data before analysis.”

Algorithms

1. Can you explain the concept of overfitting in machine learning?

This question tests your understanding of model performance.

How to Answer

Define overfitting and discuss its implications.

Example

“Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. This results in poor generalization to new data. To mitigate overfitting, I use techniques like cross-validation, regularization, and pruning.”

2. What is the purpose of cross-validation?

This question assesses your knowledge of model evaluation techniques.

How to Answer

Explain the concept and benefits of cross-validation.

Example

“Cross-validation is used to assess how a model will generalize to an independent dataset. By partitioning the data into training and validation sets multiple times, I can ensure that the model's performance is robust and not dependent on a specific subset of data.”

3. Describe a situation where you had to optimize a machine learning model. What steps did you take?

This question allows you to demonstrate your problem-solving skills.

How to Answer

Outline the optimization process, including techniques used.

Example

“In a project, I needed to optimize a classification model. I started by tuning hyperparameters using grid search and cross-validation. I also explored feature selection techniques to reduce dimensionality, which improved the model's accuracy and reduced training time.”

4. How do you choose the right algorithm for a given problem?

This question evaluates your decision-making process in model selection.

How to Answer

Discuss factors that influence your choice of algorithm.

Example

“I choose an algorithm based on the problem type, data characteristics, and performance requirements. For instance, if I have a large dataset with complex relationships, I might opt for ensemble methods like Random Forest. For simpler problems, I may use logistic regression for its interpretability.”