Handshake Data Scientist Interview Questions + Guide in 2025

Handshake is a leading career platform designed specifically for Gen Z, connecting millions of students, alumni, and employers to foster career growth and opportunities.

As a Data Scientist at Handshake, you will play a pivotal role in shaping the company's data-driven product strategies. Your responsibilities will include collaborating closely with Research and Development leadership, becoming a subject-matter expert within a specific domain, and driving analytics prioritization to measure product effectiveness. You will also lead experimentation efforts, ensuring proactive tracking and scalability of experiments, while working alongside analytics engineering teams to define metrics and build foundational data models. The ideal candidate for this role will possess strong technical expertise in machine learning and algorithms, proficiency in programming languages like Python, and an ability to communicate complex data insights effectively to both technical and non-technical stakeholders. Your contribution will be crucial in advocating for the diverse user base of Handshake, leveraging insights to inform product development and enhance the overall user experience.

This guide aims to equip you with tailored insights and strategies to excel in your interview, ensuring you can effectively demonstrate your fit for the Data Scientist role at Handshake.

The interview process for a Data Scientist role at Handshake is structured to assess both technical and interpersonal skills, ensuring candidates align with the company's values and mission. The process typically unfolds in several key stages:

1. Initial Phone Screen

The first step is a brief phone interview, usually lasting around 15-30 minutes, conducted by a recruiter. This conversation focuses on your background, interest in the role, and understanding of Handshake's mission. The recruiter will gauge your fit for the company culture and discuss your relevant experiences.

2. Take-Home Project

Following the initial screen, candidates are often required to complete a take-home data science project. This project can take several hours and is designed to evaluate your technical skills, creativity, and problem-solving abilities. You will be expected to demonstrate your proficiency in data analysis, modeling, and possibly coding, while also providing clear documentation of your thought process and findings.

3. Technical Interviews

After successfully completing the take-home project, candidates typically participate in one or more technical interviews via video conferencing. These interviews usually last around 45 minutes each and may include a coding assessment, where you will solve problems related to algorithms, data manipulation, and statistical analysis. Interviewers may also ask you to explain your approach to previous projects and how you utilized data to drive product decisions.

4. Behavioral Interviews

In addition to technical assessments, candidates will undergo behavioral interviews. These sessions focus on your past experiences, teamwork, and how you handle challenges. Expect questions that explore your communication skills, ability to collaborate with cross-functional teams, and how you advocate for data-driven decisions within a product context.

5. Final Interview Round

The final stage often involves a panel interview with multiple team members, including data scientists, product managers, and possibly leadership. This round assesses your fit within the team and your ability to contribute to Handshake's goals. You may be asked to discuss your take-home project in detail, including your methodology and insights, as well as engage in discussions about Handshake's products and analytics strategies.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your technical expertise and collaborative experiences.

In this section, we’ll review the various interview questions that might be asked during a Data Scientist interview at Handshake. Candidates should focus on demonstrating their technical expertise, analytical thinking, and ability to communicate complex data insights effectively. The questions will cover a range of topics, including machine learning, data analysis, and product analytics.

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.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

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 clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What was your role?

This question assesses your practical experience and ability to contribute to projects.

How to Answer

Outline the project’s objective, your specific contributions, and the outcomes. Emphasize your problem-solving skills and collaboration with team members.

Example

“I worked on a project to predict user engagement on our platform. My role involved feature engineering, model selection, and evaluation. I collaborated with product managers to align our findings with business goals, ultimately improving user retention by 15%.”

3. How do you handle overfitting in a machine learning model?

This question tests your understanding of model performance and validation techniques.

How to Answer

Discuss strategies such as cross-validation, regularization, and pruning. Explain how you would apply these techniques in a practical scenario.

Example

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

4. What metrics do you use to evaluate the performance of a machine learning model?

This question gauges your knowledge of model evaluation.

How to Answer

Mention various metrics relevant to the type of problem (e.g., accuracy, precision, recall, F1 score for classification tasks; RMSE or MAE for regression tasks).

Example

“I typically use accuracy and F1 score for classification models to balance precision and recall. For regression tasks, I prefer RMSE as it provides a clear measure of prediction error, allowing for better model comparison.”

Data Analysis

1. Describe your experience with SQL and how you have used it in your previous roles.

SQL proficiency is essential for this role, and this question assesses your practical skills.

How to Answer

Provide examples of complex queries you’ve written, including joins, subqueries, and aggregations. Highlight how you used SQL to derive insights from data.

Example

“I have extensive experience with SQL, including writing complex queries to extract and analyze data from multiple tables. For instance, I created a dashboard that tracked user engagement metrics by joining user activity logs with demographic data, which informed our marketing strategies.”

2. How do you approach exploratory data analysis (EDA)?

This question evaluates your analytical thinking and methodology.

How to Answer

Discuss the steps you take during EDA, such as data cleaning, visualization, and identifying trends or anomalies.

Example

“I start EDA by cleaning the data to handle missing values and outliers. Then, I use visualizations like histograms and scatter plots to explore distributions and relationships. This process helps me identify key trends and informs the direction of further analysis.”

3. Can you explain A/B testing and how you would design an experiment?

A/B testing is crucial for product analytics, and this question assesses your understanding of experimental design.

How to Answer

Explain the concept of A/B testing, including control and treatment groups, and outline the steps you would take to design a robust experiment.

Example

“A/B testing involves comparing two versions of a product to determine which performs better. I would define clear hypotheses, randomly assign users to control and treatment groups, and ensure that the sample size is statistically significant. After running the test, I would analyze the results using appropriate metrics to draw conclusions.”

4. What tools do you use for data visualization, and why?

This question assesses your familiarity with visualization tools and your ability to communicate data insights.

How to Answer

Mention specific tools you’ve used, such as Looker, Tableau, or Hex, and explain why you prefer them for certain tasks.

Example

“I primarily use Looker for its ability to create interactive dashboards that stakeholders can easily navigate. I also appreciate its integration with SQL, allowing me to pull data directly and visualize it in real-time, which enhances decision-making processes.”

Communication and Collaboration

1. How do you communicate complex data insights to non-technical stakeholders?

This question evaluates your communication skills and ability to bridge the gap between data and business.

How to Answer

Discuss strategies for simplifying complex concepts, such as using analogies, visual aids, and focusing on actionable insights.

Example

“I focus on translating technical jargon into relatable terms. For instance, when presenting data trends, I use visualizations to illustrate key points and emphasize the implications for business strategy, ensuring that stakeholders understand the relevance of the data.”

2. Describe a time when you had to collaborate with cross-functional teams. What challenges did you face?

This question assesses your teamwork and problem-solving abilities.

How to Answer

Share a specific example, highlighting the collaboration process, challenges encountered, and how you overcame them.

Example

“I collaborated with product managers and engineers on a feature rollout. One challenge was aligning our goals, as the product team prioritized speed while I emphasized data accuracy. We held regular check-ins to ensure everyone was on the same page, ultimately leading to a successful launch that met both speed and quality standards.”

3. How do you prioritize tasks when working on multiple projects?

This question evaluates your organizational skills and ability to manage time effectively.

How to Answer

Discuss your approach to prioritization, such as using frameworks or tools to assess urgency and importance.

Example

“I use a prioritization matrix to evaluate tasks based on their impact and urgency. This helps me focus on high-impact projects first while ensuring that deadlines are met. Regular communication with stakeholders also helps me adjust priorities as needed.”

4. Can you give an example of how you used data to influence a product decision?

This question assesses your ability to leverage data for strategic decision-making.

How to Answer

Provide a specific example where your data analysis led to a significant product decision or change.

Example

“During a product review, I analyzed user feedback and engagement metrics, revealing that a specific feature was underutilized. I presented this data to the product team, advocating for a redesign based on user needs. This led to a successful update that increased feature usage by 30%.”