Proficient data scientists drive strategic decisions, improve operational efficiency and enhance customer experiences. However, finding the best hire comes with its challenges.
Data scientists must have technical expertise, analytical skills, and problem-solving abilities, making the interview process complex. We provide key interview questions for data scientists and highlight how the STAR method can help you evaluate candidates effectively to ensure you get the right person for the job.
Read on to explore our data science interview cheat sheet.
Methodology
The STAR methodology is a structured approach for responding to behavioral interview questions. Familiarizing yourself with this method can provide a framework to evaluate responses when interviewing candidates.
Here's a breakdown:
- Situation: Description of the scenario or challenge the candidate encountered in a previous role.
- Task: Explanation of their tasks or responsibilities.
- Action: Details about the actions they took to address the situation.
- Result: Overview of the impact their actions had and lessons they learned.
Here's an example of a STAR response:
- Situation: In my previous role as a data scientist, my team faced declining user engagement on our platform.
- Task: I was responsible for analyzing user behavior data and identifying potential causes for the decline.
- Action: I conducted a comprehensive analysis of user interactions, segmented by demographics and usage patterns.
- Result: We successfully reversed the decline in user engagement, increasing overall platform activity by 20% within three months.
1. Can You Explain a Data Science Project You Have Worked On from Start to Finish?
Data science interviews often include project-based behavioral questions to assess candidates' technical expertise. Pay attention to problem-solving skills when listening to responses.
Why You Should Ask This Question
Asking this question can help you evaluate a candidate's ability to articulate their project experiences effectively. It can also:
- Provide insights into the tools and technology candidates are comfortable using.
- Help you assess their communication skills and ability to convey complex concepts.
- Assess their project management capabilities.
What to Expect from the Candidate
Here's what to look for in a successful answer using the STAR method:
- Situation: The project's context, including the industry or domain, challenges, and objectives.
- Task: The candidate's job duties and responsibilities relating to this project.
- Action: How the candidate addressed the project requirements using relevant tools and techniques.
- Result: A summary of the project's outcome that includes quantifiable results.
2. How Do You Handle Missing Data in a Dataset?
Addressing missing data within datasets ensures analytical models are reliable. It's crucial to clean data before analyzing it to ensure accurate results.
When candidates respond to this question, ensure they understand how essential this step is.
Why You Should Ask This Question
This question can help you determine whether a candidate knows how to clean data. It can also allow you to assess their:
- Proficiency in data preprocessing.
- Knowledge of strategies to mitigate data errors.
- Problem-solving abilities and adaptability.
What to Expect from the Candidate
A strong answer will include the following:
- Situation: A scenario involving missing data within a dataset.
- Task: The candidate's role and responsibilities in addressing data issues.
- Action: The candidate's approach to handling missing data includes techniques such as imputation or deletion.
- Result: The outcome of the candidate's data handling strategies.
3. How Do You Ensure the Quality and Integrity of Your Data?
Asking this question will help you assess whether the candidate understands the importance of data accuracy and knows what steps to take to keep your data in good shape.
Why You Should Ask This Question
This question lets you evaluate whether the candidate understands how to ensure data is trustworthy. Their answer will help you:
- Learn about their problem-solving skills.
- Determine whether they pay attention to details.
- Discover whether they understand the importance of data quality.
What to Expect from the Candidate
Here's what to look for in a candidate's response:
- Situation: An account of a time when they ensured data was good quality.
- Task: What they did to ensure the data was accurate.
- Action: The steps they took in checking the data, fixing any issues and making sure it met quality standards.
- Result: The outcome after they worked on the data and the impact on their analysis.
4. Can You Explain the Difference between Supervised and Unsupervised Learning?
Understanding the difference between supervised and unsupervised learning is fundamental to machine learning. This question can help you determine whether candidates understand these two essential concepts.
Why You Should Ask This Question
This question allows you to assess the candidate's familiarity with basic machine-learning concepts. It can also help you:
- Evaluate their ability to explain practical applications.
- Gauge their theoretical knowledge of machine-learning topics.
- Determine their experience applying these techniques to real-world problems.
What to Expect from the Candidate
When listening to the candidate's response, pay attention to whether they include the following:
- Situation: A scenario where they had to choose between supervised and unsupervised learning for a project.
- Task: The specific problem they were trying to solve.
- Action: The steps they took to implement either a supervised or unsupervised learning approach.
- Result: The outcome of their approach, including the accuracy or insights gained from the model they chose to use.
Elevate Your Tech Recruitment Process
By asking the right questions and evaluating responses using the STAR method, you can hire skilled data scientists who unlock valuable insights for your organization. Here's a summary of key points:
- Look for candidates with a unique blend of technical expertise and problem-solving skills.
- Ask questions about projects, data preprocessing, quality and integrity, machine learning and fundamental concepts.
- Use the STAR methodology to provide a structured framework when evaluating responses.
- Focus on behavioral questions that provide deeper insight into each candidate's work experience and skill level.
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