Effective Work Sample Exercises for Hiring Top Data Scientists

Data Scientists are the analytical backbone of modern organizations, transforming raw data into actionable insights that drive strategic decisions. The best Data Scientists combine technical expertise in statistics, mathematics, and computer science with strong business acumen and communication skills. Finding candidates who truly possess this rare combination of skills requires more than just reviewing resumes and conducting standard interviews.

Traditional interviews often fail to reveal a candidate's true capabilities in handling real-world data challenges. A candidate might eloquently discuss machine learning algorithms or statistical methods but struggle when faced with messy datasets or ambiguous business problems. This disconnect between interview performance and on-the-job success can lead to costly hiring mistakes.

Work sample exercises provide a window into how candidates actually approach data science problems. By observing candidates as they clean data, build models, plan projects, and communicate findings, hiring teams can gain valuable insights into their technical abilities, problem-solving approaches, and communication styles. These exercises simulate the day-to-day responsibilities of a Data Scientist, allowing for a more accurate assessment of a candidate's potential contribution to your team.

The following four work sample exercises are designed to evaluate the essential competencies required for success as a Data Scientist. Each exercise targets specific skills, from technical implementation to strategic planning and communication. By incorporating these exercises into your hiring process, you'll be better equipped to identify candidates who can not only analyze data effectively but also translate those analyses into business value.

Activity #1: Predictive Modeling Challenge

This exercise evaluates a candidate's ability to work with real-world data, apply appropriate machine learning techniques, and communicate their approach and findings. It tests technical skills in data cleaning, feature engineering, model selection, and evaluation—core competencies for any Data Scientist.

Directions for the Company:

• Prepare a dataset with a clear prediction task (e.g., customer churn prediction, sales forecasting, or product recommendation).
• The dataset should include some common challenges like missing values, outliers, or categorical variables that require preprocessing.
• Provide a clear business context and objective for the prediction task.
• Allow candidates 2-3 hours to complete the exercise, either as a take-home assignment or during an on-site interview.
• Prepare a Jupyter notebook template with sections for data exploration, preprocessing, modeling, evaluation, and business recommendations.
• Include evaluation criteria that focus on both technical implementation and business relevance of the solution.

Directions for the Candidate:

• Explore and clean the provided dataset, documenting your approach to handling missing values, outliers, and other data quality issues.
• Engineer relevant features that might improve model performance.
• Build and evaluate at least two different machine learning models for the prediction task.
• Select the best-performing model and explain your choice based on appropriate evaluation metrics.
• Interpret the model results in business terms, highlighting key insights and potential actions.
• Prepare a brief (5-minute) presentation of your approach and findings for a non-technical audience.
• Submit your code, documentation, and presentation slides.

Feedback Mechanism:

• After the candidate presents their solution, provide specific feedback on one technical aspect they handled well (e.g., feature engineering approach, model selection) and one area for improvement (e.g., handling of outliers, model interpretation).
• Ask the candidate to spend 10-15 minutes implementing the suggested improvement or explaining how they would approach it differently.
• Observe how receptive the candidate is to feedback and their ability to adapt their approach.

Activity #2: Data Science Project Planning

This exercise assesses a candidate's ability to plan and structure complex data science initiatives, a critical skill for ensuring projects deliver business value. It evaluates strategic thinking, project management capabilities, and understanding of the end-to-end data science workflow.

Directions for the Company:

• Create a scenario describing a business problem that requires a data science solution (e.g., developing a recommendation system, optimizing a supply chain, or predicting equipment failures).
• Include details about available data sources, stakeholder expectations, and potential challenges.
• Provide a template for the project plan that includes sections for problem definition, data requirements, methodology, timeline, resource needs, and success metrics.
• Allow 45-60 minutes for this exercise.
• Prepare questions to probe the candidate's reasoning behind their planning decisions.

Directions for the Candidate:

• Review the business scenario and identify the key objectives for the data science project.
• Outline the major phases of the project, from problem definition to deployment and monitoring.
• Specify the data requirements, including sources, quality checks, and preprocessing needs.
• Propose appropriate methodologies and techniques for addressing the business problem.
• Develop a realistic timeline with key milestones and dependencies.
• Identify potential risks and challenges, along with mitigation strategies.
• Define clear success metrics that align with business objectives.
• Be prepared to explain your rationale for each element of the project plan.

Feedback Mechanism:

• Provide feedback on one strength of the project plan (e.g., comprehensive risk assessment, well-defined success metrics) and one area that needs refinement (e.g., unrealistic timeline, missing data considerations).
• Ask the candidate to revise the identified area of the project plan based on your feedback.
• Evaluate their ability to incorporate feedback while maintaining the overall coherence of the plan.

Activity #3: Stakeholder Communication Role Play

This exercise evaluates a candidate's ability to communicate complex technical concepts to non-technical stakeholders—a crucial skill for ensuring data science work drives business decisions. It tests communication clarity, adaptability, and business acumen.

Directions for the Company:

• Prepare a scenario where the candidate must explain the results of a data analysis or machine learning model to a business stakeholder.
• Create a one-page summary of the analysis results, including visualizations, model performance metrics, and technical details.
• Assign a company representative to play the role of a business stakeholder (e.g., marketing director, product manager, or C-suite executive).
• Brief the role player on specific questions to ask, including some that challenge the analysis or request clarification on technical concepts.
• Allow the candidate 15 minutes to review the materials before the 20-minute role play.

Directions for the Candidate:

• Review the provided analysis results and prepare to explain them to a non-technical stakeholder.
• During the role play, clearly communicate the key findings and their business implications.
• Translate technical concepts into business language without oversimplifying or losing important nuances.
• Use visualizations effectively to support your explanation.
• Listen carefully to the stakeholder's questions and concerns, addressing them appropriately.
• Make specific recommendations based on the analysis results.
• Be prepared to adapt your communication style based on the stakeholder's level of understanding and interests.

Feedback Mechanism:

• After the role play, provide feedback on one aspect of the communication that was effective (e.g., clear explanation of a complex concept, good use of analogies) and one area for improvement (e.g., too much technical jargon, insufficient focus on business implications).
• Give the candidate 5-10 minutes to re-explain a specific part of the analysis, incorporating the feedback.
• Assess their ability to adapt their communication approach while maintaining accuracy.

Activity #4: Causal Inference and A/B Test Design

This exercise assesses a candidate's understanding of experimental design and causal inference—critical skills for data-driven decision making. It evaluates statistical thinking, experimental design capabilities, and understanding of the limitations of observational data.

Directions for the Company:

• Create a business scenario that requires determining the causal impact of a change or intervention (e.g., a new feature, pricing strategy, or marketing campaign).
• Provide background information on the business context, available data, and key metrics.
• Include constraints or challenges that make the causal question non-trivial (e.g., limited sample size, potential confounders, or ethical considerations).
• Prepare a template for the candidate to document their experimental design.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Analyze the business scenario and clearly articulate the causal question to be answered.
• Design an appropriate experimental or quasi-experimental approach to estimate the causal effect.
• If proposing an A/B test:
• Define the treatment and control conditions
• Specify the unit of randomization
• Calculate the required sample size and duration
• Identify key metrics and success criteria
• Outline the randomization procedure
• If using observational data:
• Identify potential confounders and how to address them
• Propose appropriate causal inference methods (e.g., matching, difference-in-differences, instrumental variables)
• Discuss assumptions and limitations of your approach
• Explain how you would validate the results and address potential threats to validity.
• Outline how you would communicate the findings to stakeholders.

Feedback Mechanism:

• Provide feedback on one strength of the experimental design (e.g., thoughtful consideration of confounders, appropriate sample size calculation) and one area for improvement (e.g., overlooked interaction effects, insufficient power analysis).
• Ask the candidate to revise the identified aspect of their design based on your feedback.
• Evaluate their understanding of causal inference principles and ability to refine their approach.

Frequently Asked Questions

How long should each work sample exercise take?

The predictive modeling challenge typically requires 2-3 hours and works well as a take-home assignment. The project planning, stakeholder communication, and causal inference exercises can each be completed in 45-60 minutes during an on-site interview. Consider spreading these exercises across different interview stages to avoid candidate fatigue.

Should we use real company data for these exercises?

While using real data can make the exercises more relevant, it's often better to use anonymized or synthetic data that resembles your actual data. This protects confidential information while still testing relevant skills. If using real data is important, consider having candidates sign an NDA before accessing the data.

How should we evaluate candidates who use different technical approaches than we expected?

Focus on the reasoning behind their choices rather than whether they used a specific technique. A candidate who can clearly explain why they chose a particular approach—even if it differs from your team's standard methods—demonstrates valuable critical thinking. The most important factors are whether their approach is valid for the problem and whether they can articulate its strengths and limitations.

What if a candidate struggles with the feedback portion of the exercise?

How a candidate responds to feedback can be as informative as their initial performance. If they struggle to incorporate feedback, this might indicate challenges with adaptability or coachability. However, consider whether the feedback was clear and actionable, and whether the time constraints were reasonable. Some candidates might perform better with written feedback and more time to reflect.

Should we standardize the evaluation criteria across all candidates?

Yes, using a consistent rubric for each exercise ensures fair comparison across candidates. The rubric should include both technical criteria (e.g., code quality, statistical rigor) and non-technical aspects (e.g., communication clarity, business understanding). Having multiple evaluators independently score each candidate using the same rubric can also help reduce individual biases.

How can we make these exercises accessible to candidates with different backgrounds?

Ensure that the exercises don't unnecessarily favor candidates from specific backgrounds. For example, if your data science team uses Python but a qualified candidate is more experienced with R, consider allowing them to use their preferred language. Similarly, be mindful of time constraints for candidates with caregiving responsibilities or disabilities that might affect their work pace.

Implementing these work sample exercises will significantly improve your ability to identify Data Scientists who can not only analyze data effectively but also translate those analyses into business value. By observing candidates as they tackle realistic challenges, you'll gain insights into their technical skills, problem-solving approaches, communication abilities, and adaptability—all critical factors for success in this role.

Ready to take your Data Scientist hiring process to the next level? Yardstick offers AI-powered tools to help you create customized job descriptions, generate targeted interview questions, and design comprehensive interview guides. Check out our AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator to streamline your hiring process. For more insights on hiring Data Scientists, explore our Data Scientist job description template.

Build a complete interview guide for this role by signing up for a free Yardstick account here