Essential Work Sample Exercises for Evaluating AI Employee Retention Specialists

Employee turnover represents one of the most significant challenges facing organizations today, with replacement costs often exceeding 150% of an employee's annual salary. As companies increasingly turn to artificial intelligence to identify retention risks before they materialize, the demand for specialists who can develop and implement these AI systems has grown exponentially. However, finding candidates with the right blend of technical AI expertise and human resources domain knowledge presents a unique hiring challenge.

Traditional interviews often fail to reveal a candidate's true capabilities in this specialized field. While a candidate may articulate theoretical knowledge about machine learning algorithms or retention strategies, their ability to apply these concepts in real-world scenarios remains untested. This gap between theoretical understanding and practical application can lead to costly hiring mistakes and implementation failures.

Work sample exercises provide a window into how candidates approach the multifaceted challenges of AI-driven retention risk identification. These exercises reveal not just technical proficiency, but also critical thinking, ethical awareness, and communication skills essential for translating complex AI insights into actionable retention strategies that business leaders can understand and implement.

The following work samples are designed to evaluate candidates across the full spectrum of skills needed for successful AI retention risk identification. From model design and feature engineering to dashboard creation and ethical framework development, these exercises simulate the actual challenges these specialists face daily. By observing candidates work through these realistic scenarios, hiring managers can make more informed decisions about which individuals possess both the technical capabilities and domain expertise to drive meaningful improvements in employee retention.

Activity #1: Retention Risk Model Design

This exercise evaluates a candidate's ability to design a comprehensive AI system for predicting employee retention risks. It tests their understanding of machine learning approaches suitable for retention prediction, their knowledge of relevant data sources, and their ability to plan a complex technical project while considering business objectives. This foundational skill determines whether a candidate can architect effective AI retention solutions from the ground up.

Directions for the Company:

• Provide the candidate with a fictional company profile including industry, size (approximately 2,000 employees), current turnover rate (18%), and business objectives related to retention.
• Include a basic data inventory listing available data sources (HRIS data, performance reviews, engagement surveys, etc.) with sample fields for each.
• Allow 45-60 minutes for this exercise.
• Prepare a conference room with whiteboard or digital collaboration tools.
• Have a technical interviewer and an HR leader present to evaluate both technical and domain-specific aspects.

Directions for the Candidate:

• Design a comprehensive approach for building an AI-based employee retention risk identification system for the provided company.
• Create a detailed diagram showing the proposed model architecture, data pipeline, and implementation process.
• Specify which machine learning algorithms you would consider and why they're appropriate for retention prediction.
• Identify which data sources and features would be most predictive and explain your reasoning.
• Outline how you would validate the model's effectiveness and measure ROI.
• Prepare to present your design in 15 minutes, followed by 15 minutes of questions.

Feedback Mechanism:

• After the presentation, provide one piece of positive feedback about an aspect of their design that was particularly strong or innovative.
• Offer one constructive suggestion for improvement, such as an overlooked data source, a potential bias in the model, or an implementation challenge they didn't address.
• Give the candidate 10 minutes to revise their approach based on this feedback, focusing specifically on the improvement area identified.
• Observe how receptively they incorporate feedback and their ability to adapt their thinking.

Activity #2: Feature Engineering for Retention Prediction

This exercise assesses a candidate's ability to identify, transform, and select the most relevant features for retention prediction models. It tests their understanding of which employee data points are most predictive of turnover risk and their technical skills in preparing this data for machine learning algorithms. This tactical implementation skill is crucial for building models that accurately identify at-risk employees before they resign.

Directions for the Company:

• Prepare a sanitized dataset (with all personally identifiable information removed) containing employee attributes and historical retention outcomes. Include approximately 50-100 rows with 15-20 columns.
• The dataset should include a mix of:
• Demographic information (tenure, department, level, etc.)
• Performance metrics (ratings, goal achievement)
• Engagement indicators (survey scores, participation metrics)
• Compensation data (salary, last raise percentage, compensation ratio)
• Career progression metrics (time in role, promotions)
• A target column indicating whether employees left within 6 months
• Provide access to a Jupyter notebook environment or similar tool with necessary libraries installed.
• Allow 60 minutes for this exercise.

Directions for the Candidate:

• Analyze the provided dataset to identify which features are most predictive of employee turnover.
• Perform necessary data cleaning, transformation, and feature engineering to prepare the data for a machine learning model.
• Create at least 3 new derived features that you believe would improve prediction accuracy.
• Use appropriate techniques to select the most relevant features for the final model.
• Document your process, explaining the rationale behind each transformation and selection decision.
• Prepare a brief explanation of which features appear most predictive of turnover risk and why this aligns with or challenges common retention theories.

Feedback Mechanism:

• Provide positive feedback on one aspect of their feature engineering approach that demonstrated particular insight or technical skill.
• Offer one specific suggestion for improvement, such as an overlooked transformation, a potential feature interaction, or a more efficient approach to a particular step.
• Give the candidate 15 minutes to implement this specific improvement and explain how it enhances the overall approach.
• Evaluate their technical agility and domain knowledge based on how they incorporate this feedback.

Activity #3: Retention Risk Dashboard Creation

This exercise evaluates a candidate's ability to translate complex AI predictions into actionable visualizations that business leaders and HR professionals can use to drive retention strategies. It tests their data visualization skills, business acumen, and ability to communicate technical insights to non-technical stakeholders. This skill is essential for ensuring AI retention models actually influence decision-making and improve retention outcomes.

Directions for the Company:

• Prepare a dataset containing retention risk predictions for 200 fictional employees, including:
• Employee IDs and basic information (department, role, tenure, etc.)
• Retention risk scores (probability of leaving within 6 months)
• Key contributing factors to each risk score (e.g., compensation, engagement, career growth)
• Historical retention rates by department and role
• Provide access to a data visualization tool (Tableau, Power BI, or similar) with which the candidate is familiar.
• Allow 60-75 minutes for this exercise.
• Prepare a list of key stakeholders who would use this dashboard (CHRO, department heads, HR business partners).

Directions for the Candidate:

• Design an interactive dashboard that effectively communicates retention risk insights to HR leaders and managers.
• Create visualizations that highlight:
• Overall retention risk distribution across the organization
• High-risk employee clusters by department, role, or other relevant dimensions
• Primary drivers of retention risk for different employee segments
• Recommended intervention priorities based on risk levels and business impact
• Include appropriate filters and drill-down capabilities for different user needs.
• Ensure the dashboard balances comprehensive information with clarity and usability.
• Prepare a 10-minute presentation explaining how different stakeholders would use this dashboard to reduce turnover.
• Address how the dashboard protects employee privacy while providing actionable insights.

Feedback Mechanism:

• Provide positive feedback on one aspect of their dashboard design that effectively communicates complex retention insights.
• Offer one specific suggestion for improvement, such as an additional visualization that would enhance understanding, a usability enhancement, or a way to make the insights more actionable.
• Give the candidate 15 minutes to implement this specific improvement and explain how it enhances the dashboard's effectiveness.
• Evaluate their ability to balance technical sophistication with user-centered design and business relevance.

Activity #4: Ethical Framework Development

This exercise assesses a candidate's ability to address the ethical implications of using AI for employee retention prediction. It tests their understanding of potential biases, privacy concerns, and responsible AI practices in the HR context. This strategic planning skill is crucial for implementing retention AI systems that are not only effective but also fair, transparent, and aligned with organizational values.

Directions for the Company:

• Prepare a scenario description of an organization planning to implement an AI retention prediction system that includes:
• The company's diversity goals and current challenges
• Planned uses of the system (informing compensation decisions, targeting retention programs, etc.)
• Available data sources (including potentially sensitive information)
• Key stakeholder concerns (from legal, employee representatives, executives)
• Provide relevant materials on AI ethics frameworks and HR data privacy regulations.
• Allow 60 minutes for this exercise.
• Have both technical and HR leadership representatives present for the discussion.

Directions for the Candidate:

• Develop a comprehensive ethical framework for implementing AI-based retention risk identification at the described organization.
• Address the following key areas:
• Potential sources of bias in the model and mitigation strategies
• Data privacy protections and compliance considerations
• Transparency and explainability approaches for both HR users and employees
• Guidelines for appropriate use cases and limitations
• Governance structure for ongoing ethical oversight
• Create a one-page visual representation of your ethical framework that could be shared with stakeholders.
• Prepare to present your framework and answer questions about specific ethical dilemmas that might arise.
• Include recommendations for measuring and monitoring the ethical performance of the system over time.

Feedback Mechanism:

• Provide positive feedback on one aspect of their ethical framework that demonstrates particular insight or thoughtfulness.
• Present a specific ethical challenge not fully addressed in their framework (e.g., a specific bias scenario, a complex privacy issue, or a difficult stakeholder objection).
• Give the candidate 15 minutes to develop a targeted approach to this specific challenge and incorporate it into their framework.
• Evaluate their ability to balance ethical considerations with practical implementation needs and their awareness of nuanced ethical implications in AI-driven HR systems.

Frequently Asked Questions

How much technical AI knowledge should candidates demonstrate in these exercises?

Candidates should demonstrate sufficient technical knowledge to design and implement effective AI retention models, but the emphasis should be on applying that knowledge to the specific challenges of employee retention. Look for their ability to select appropriate algorithms for this domain, understand which features matter for retention prediction, and translate technical outputs into business insights, rather than testing advanced AI theory.

Should we provide real company data for these exercises?

No, always use fictional or thoroughly anonymized data. Create synthetic datasets that realistically represent employee data patterns but contain no actual employee information. This protects confidentiality while still allowing candidates to demonstrate their skills in a realistic context.

How should we evaluate candidates who approach these exercises differently than expected?

Innovative approaches often indicate exceptional candidates. Evaluate based on the soundness of their reasoning, not adherence to a predetermined solution. If a candidate chooses unexpected features or visualization approaches but can articulate a compelling rationale based on retention research or AI best practices, this may demonstrate valuable creative thinking and domain expertise.

What if candidates don't have experience with our specific tools (e.g., our visualization platform)?

Focus on evaluating the underlying skills rather than tool-specific knowledge. Allow candidates to use tools they're familiar with when possible, or provide brief tutorials for your specific tools. The core abilities—identifying predictive features, designing effective visualizations, and developing ethical frameworks—transfer across platforms and are more important than specific tool expertise.

How should we balance evaluating technical AI skills versus HR domain knowledge?

The most successful AI retention specialists possess both technical skills and domain expertise. These exercises are designed to evaluate this intersection. When evaluating responses, consider whether candidates demonstrate understanding of both the technical aspects (appropriate algorithms, feature engineering) and the human aspects (what actually drives retention, how leaders will use insights). The ideal candidate shows strength in both areas, but different roles may prioritize one aspect over the other.

Should we expect candidates to complete all aspects of these exercises perfectly?

No. These exercises are intentionally comprehensive to observe how candidates approach complex challenges. Focus on their problem-solving process, the questions they ask, and how they prioritize different aspects of the problem given time constraints. Their response to feedback often reveals more about their potential for success than their initial solution.

The hiring process for AI retention specialists represents a significant investment in your organization's future ability to retain top talent. By implementing these work sample exercises, you gain unprecedented insight into how candidates will actually perform in this multifaceted role. The exercises evaluate not just technical AI capabilities, but also the crucial ability to apply these technologies to the human-centered challenge of employee retention.

For organizations serious about leveraging AI to improve retention, finding the right talent is the essential first step. These work samples help you identify candidates who can bridge the gap between advanced AI techniques and practical HR applications—professionals who will build systems that not only predict retention risks but drive meaningful interventions that keep your best people engaged and committed.

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