Introduction

Case Assessment

Effective Case Response

Lyft Example

Preparation Strategy

What Is a Data Science Case?

[Applied Statistics] Survey Weighting

[Machine Learning] Regression Modeling Framework

[Marketing] Attribution Modeling

[Marketing] Marketing Mix Modeling - Part 1

[Marketing] Marketing Mix Modeling - Part 2

[Marketing] Measuring Marketing Performance

Technical Case Frameworks

[Amazon] Evaluate Variance

[Meta] Distribution of Comments

[Meta] Distribution of False Positives

[Meta] Parents on Facebook

[Meta] Revenue Analytics on Facebook Games

Applied Statistics

[Google] Search Metric Shifts

[Instacart] Measure Active Users

[Instagram] Measure Success on Stories

[LinkedIn] Engagement Increased, but Revenue Decreased

[LinkedIn] Estimating Business Travelers

[Lyft] Measuring Driver Satisfaction

[Meta] Decreasing User Retention After Sign-Ups

[Meta] Facebook Groups

[Meta] Measuring Performance of News Feed

[Meta] Measuring Spam on Facebook

[Meta] P-Value > Significance Level (Alpha)

[Netflix] Improve Conversions

[Netflix] Long-Term Metric

[Uber] Rideshare Engagement

Product Metrics

[Amazon] AB Testing Multiple Changes

[Amazon] Change Declined After Launch

[Google] How would you improve Google Maps?

[Google] Peeking at the P-Value

[Hotel Tonight] Metric Change

[Instacart] Statistical Parameters in AB Testing

[LinkedIn] Premium Subscription Funnel

[LinkedIn] Should LinkedIn Have Videos?

[Meta] How would you improve Facebook Messenger?

[Netflix] Video Preview

[Uber Eats] Measure Long-Term Effects

[Uber] Incentivizing Inactive Users on Uber Eats

Experimentation

[Amazon] Detect Fraudulent Users

[Amazon] Estimate Customer Lifetime Value

[Amazon] Sales Forecasting

[Apple] Meaningful Customer Segmentation

[Google] Forecast Airline Ticket Prices

[LinkedIn] Optimize Email Campaigns

[LinkedIn] Salary Estimation

[Meta] Fraud Features

[Meta] Optimize Cold Sales Calls

[Redfin] Pricing Optimization

Machine Learning

[Instacart] Free Delivery Opportunity Sizing

[Meta] Portal Launch Opportunity Sizing

Opportunity Sizing

[Apple] Marketing Mix Modeling on iPhone Sales

[CVS] Marketing Outreach Experiment

[LinkedIn] Email Conversions

[Uber] Measure the Impact of Super Bowl Ads

Marketing

[AWS] Propensity Modeling

[BCG Gamma] Customer Satisfaction Survey

[McKinsey] Employee Attrition

Consulting Cases

Case-In-Point


<h2 class="wp-block-heading"><img decoding="async" src="https://files.cdn.thinkific.com/file_uploads/481328/images/05d/5ad/c48/DataInterview_Case_Cover.png" srcset="https://files.cdn.thinkific.com/file_uploads/481328/images/05d/5ad/c48/DataInterview_Case_Cover.png?width=1920 1x, https://files.cdn.thinkific.com/file_uploads/481328/images/05d/5ad/c48/DataInterview_Case_Cover.png?width=1920&amp;dpr=2 2x, https://files.cdn.thinkific.com/file_uploads/481328/images/05d/5ad/c48/DataInterview_Case_Cover.png?width=1920&amp;dpr=3 3x"></h2>



<h2 class="wp-block-heading"><strong>Problem&nbsp;🎯&nbsp;</strong></h2>



<p>An interviewer at&nbsp;<strong>McKinsey</strong>&nbsp;asked:</p>



<p>Using employee data such as tenure, performance reviews, and job role, predict which employees are most likely to leave the company in the next year. Recommend strategies to increase retention.</p>



<h2 class="wp-block-heading"><strong>Tips💡&nbsp;&nbsp;</strong></h2>



<ol class="wp-block-list">
<li><strong>Consider All Data Dimensions</strong>Remember, employee attrition isn&#8217;t only influenced by direct factors like performance reviews. Are there any potential external factors or additional datasets that might add context?</li>



<li><strong>Model Complexity and Interpretability</strong>:While advanced models might yield better accuracy, consider the trade-offs. How will stakeholders use and interpret your model&#8217;s results? Sometimes, an interpretable model that business leaders can understand and act upon might be more valuable than a slightly more accurate but opaque one.</li>



<li><strong>Tailored Retention Strategies</strong>:Once you identify the key variables influencing attrition, think about how the company can address each specific factor. For instance, if tenure emerges as a significant factor, what strategies can address both new and longtime employees? Avoid generic solutions and consider targeted interventions that can be piloted and scaled based on their success.</li>
</ol>



<h2 class="wp-block-heading"><strong>Solution&nbsp;✍️&nbsp;&nbsp;</strong></h2>



<p><strong>[Interviewer]</strong>&nbsp;Using employee data such as tenure, performance reviews, and job role, I&#8217;d like you to predict which employees are most likely to leave the company in the next year. Once you have your prediction, I&#8217;d also like you to recommend strategies to increase retention.</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<p><strong>[Commentary]</strong>&nbsp;A naive response is approaching the case without clarification. &#8220;In order to predict which employees are likely to leave the company next year, I would suggest creating a simple model using a linear regression approach.</p>



<p>We can input factors like tenure, performance reviews, and job role to get a prediction score. Employees with higher scores could be deemed more likely to leave. To increase retention, the company might consider offering bonuses or salary raises to those identified as &#8216;at-risk&#8217; of leaving, or maybe even provide additional training programs to help them feel more engaged and integrated.&#8221;</p>



<p>This response demonstrates a rudimentary understanding of the problem and potential solutions but lacks depth in terms of methodology, data preparation, and specificity in recommendations.</p>



<p>Now, let’s take a look at how a candidate should approach such a case problem.</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<p><strong>[Candidate]</strong>&nbsp;Certainly. Before we get started, I have a few clarifying questions. First, do we have any historical data on employees who have left the company in the past, specifically on reasons they may have cited for leaving?</p>



<p><strong>[Interviewer]</strong>&nbsp;Yes, we have historical data for the last five years, including exit surveys which may contain information on reasons for departure.</p>



<p><strong>[Candidate]</strong>&nbsp;Great! Are there any other variables in the dataset, apart from tenure?</p>



<p><strong>[Interviewer]</strong>&nbsp;We also have data on employee profile, career progression, performance reviews, compensation &amp; promotions, and anonymized surveys.</p>



<p><strong>[Candidate]</strong>&nbsp;Noted. Regarding the performance reviews, how are they structured?</p>



<p><strong>[Interviewer]</strong>&nbsp;They contain a combination of numerical and, an optional, textual responses from the employees’ managers.</p>



<p><strong>[Candidate]</strong>&nbsp;Thank you. Based on the inputs you provided, I’d like to cover the following aspects in this discussion: data preparation, feature engineering, feature selection, modeling and evaluation. Is that okay with you?</p>



<p><strong>[Interviewer]</strong>&nbsp;Yes, that sounds good.</p>



<p><strong>[Candidate]</strong>&nbsp;First of all, I’d like to be concrete on the data sources that are available for modeling. I would imagine that we would have the following data:</p>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Employee Profile</th><th>Data Type</th><th>Example</th></tr></thead><tbody><tr><td>Employee ID</td><td>UUID</td><td>123</td></tr><tr><td>Age</td><td>Numeric</td><td>29</td></tr><tr><td>Gender</td><td>Categorical</td><td>Female</td></tr><tr><td>Education</td><td>Categorical</td><td>Undergraduate</td></tr><tr><td>Ethnicity</td><td>Categorical</td><td>Asian</td></tr><tr><td>Tenure (in Years)</td><td>Numeric</td><td>3</td></tr></tbody></table></figure>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Employee Progression</th><th>Data Type</th><th>Example</th></tr></thead><tbody><tr><td>Employee ID</td><td>UUID</td><td>123</td></tr><tr><td>Promotion Year</td><td>Numeric</td><td>2022</td></tr><tr><td>Job</td><td>Categorical</td><td>Data Scientist</td></tr><tr><td>Level</td><td>Categorical</td><td>L5</td></tr><tr><td>Team</td><td>Categorical</td><td>Data &amp; Analytics</td></tr><tr><td>Department</td><td>Categorical</td><td>Engineering</td></tr></tbody></table></figure>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Performance Review</th><th>Data Type</th><th>Example</th></tr></thead><tbody><tr><td>Employee ID</td><td>UUID</td><td>123</td></tr><tr><td>Manager ID</td><td>UUID</td><td>234</td></tr><tr><td>Review Year</td><td>Numeric</td><td>2022</td></tr><tr><td>Quality of Work (Rating: 1-5)</td><td>Numeric</td><td>5</td></tr><tr><td>Communication (Rating: 1-5)</td><td>Numeric</td><td>4</td></tr><tr><td>Judgement &amp; Decision Making (Rating: 1-5)</td><td>Numeric</td><td>4</td></tr><tr><td>Cooperation &amp; Teamwork</td><td>Numeric</td><td>4</td></tr><tr><td>Additional Comments</td><td>Text</td><td>“The employee needs to be more attentive…”</td></tr></tbody></table></figure>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Compensation and Benefits</th><th>Data Type</th><th>Example</th></tr></thead><tbody><tr><td>Employee ID</td><td>UUID</td><td>123</td></tr><tr><td>Year</td><td>Numeric</td><td>2023</td></tr><tr><td>Salary (USD)</td><td>Numeric</td><td>150000</td></tr><tr><td>Bonus (USD)</td><td>Numeric</td><td>10000</td></tr><tr><td>Equity (USD)</td><td>Numeric</td><td>50000</td></tr><tr><td>Health Insurance Plan</td><td>Categorical</td><td>Anthem</td></tr></tbody></table></figure>



<figure class="wp-block-table"><table class="has-fixed-layout"><thead><tr><th>Employee Satisfaction Surveys</th><th>Data Type</th><th>Example</th></tr></thead><tbody><tr><td>Year</td><td>Numeric</td><td>2022</td></tr><tr><td>Department</td><td>Categorical</td><td>Data &amp; Analytics</td></tr><tr><td>Satisfaction Score</td><td>Numeric</td><td>4.3</td></tr></tbody></table></figure>



<p><strong>[Interviewer]</strong>&nbsp;Great, that’s quite comprehensive. What kind of exploratory data analysis would you do?</p>



<p><strong>[Candidate]</strong>&nbsp;Thank you. I would first conduct exploratory data analysis to understand the distribution of the variables, identify any missing data, outliers, and potentially establish some preliminary relationships. For instance, I&#8217;d be interested in checking if there&#8217;s a trend between tenure and leaving the company, or if lower performance reviews correlate with higher attrition rates.</p>



<p><strong>[Interviewer]</strong>&nbsp;Sounds like a good approach. Now, what’s your next step?</p>



<p><strong>[Candidate]</strong>&nbsp;The next step is to prepare the data for the downstream process in model training. I would look for missing values per column, and apply imputation using mode value for categorical and mean value for numeric. I would also screen for outliers in numeric field to ensure that we aren’t seeing abnormal data points which may affect the model training later. One other aspect I want to cover is that this is the part where I would also create the train and holdout split. This is vital so that in the feature engineering and selection stage, I am not introducing label leakage.</p>



<p><strong>[Interviewer]</strong>&nbsp;Great! Now, how would you consider the feature engineering and selection steps?</p>



<p><strong>[Candidate]</strong>&nbsp;For categorical, I could apply one-hot encoding, for text, I could apply bag-of-words or TF-IDF. For numeric, I could apply window-based aggregations and lags to capture how an employee has progressed over time. E.g. I could create a moving average for the employee’s performance rating. This will help the model understand whether the performance of an employee has improved or declined over time. And, I would imagine that it would be a core indicator that helps discern whether an employee will stick around next year. For feature selection, I think interpretability matters, so I would avoid PCA, rather use filter or wrapper approach to identify subset of features for the model.</p>



<p><strong>[Interviewer]</strong>&nbsp;How would you tackle the prediction part?</p>



<p><strong>[Candidate]</strong>&nbsp;Given that this is a binary classification problem &#8211; predicting whether an employee will leave or stay &#8211; I would consider algorithms such as logistic regression, random forests, or gradient-boosted trees. But the choice will largely depend on the results of the EDA and the characteristics of our dataset, like size and imbalance.</p>



<p><strong>[Interviewer]</strong>&nbsp;And how would you evaluate the effectiveness of your model?</p>



<p><strong>[Candidate]</strong>&nbsp;To evaluate the model, I would split the data into training and test sets. After training, I would measure its performance on the test set using metrics like accuracy, precision, recall, F1-score, and AUC-ROC. Since retaining valuable employees is crucial, I&#8217;d particularly pay attention to recall to ensure we&#8217;re capturing as many actual leavers as possible.</p>



<p><strong>[Interviewer]</strong>&nbsp;Can you tell me what would be the pros and cons of using accuracy for the model training?</p>



<p>[Candidate] Yes, the benefit of accuracy is that it is easy to interpret. But, it’s not a good metric for the model to optimize on, especially when the labels are imbalanced. So you want to use other metrics such as AUC to get a sense of whether the model has corrected detected employees who will stay next year but also those who will leave next year.</p>



<p><strong>[Interviewer]</strong>&nbsp;Once you&#8217;ve identified the employees likely to leave, what retention strategies might you recommend?</p>



<p><strong>[Candidate]</strong>&nbsp;While the exact recommendations would depend on the model&#8217;s findings, some general strategies might include:</p>



<ol class="wp-block-list">
<li><strong>Targeted Training and Development</strong>: If we find certain roles have higher attrition, we might offer specialized training or career development opportunities.</li>



<li><strong>Re-evaluating Compensation</strong>: If compensation emerges as a significant factor, competitive salary packages or bonuses could be considered.</li>



<li><strong>Mentoring and Feedback</strong>: Engage employees with regular feedback sessions and possibly introduce a mentorship program.</li>



<li><strong>Work-Life Balance Initiatives</strong>: Introduce or emphasize flexible working hours, remote working options, or additional leaves.</li>
</ol>



<p>Lastly, I&#8217;d suggest deploying a feedback mechanism to continuously learn from the employees and refine our retention strategies.</p>



<p><strong>[Interviewer]</strong>&nbsp;Impressive approach! I look forward to seeing how you&#8217;d handle such a task in a real-world scenario.</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<h2 class="wp-block-heading"><strong>Assessment&nbsp;📝&nbsp;</strong></h2>



<p>The candidate&#8217;s overall performance is outstanding across the evaluated categories. They showed a robust understanding of technical aspects, married it well with business context, and communicated with clarity.</p>



<h3 class="wp-block-heading"><strong>Business Sense &amp; Insights</strong></h3>



<p>Rating: Outstanding</p>



<p>The candidate displayed a good grasp of the business context by not only addressing the technical task but also considering the bigger picture of employee retention.</p>



<h3 class="wp-block-heading"><strong>Communication &amp; Presentation Skills</strong></h3>



<p>Rating: Outstanding</p>



<p>The candidate communicated complex methods, intentions, and insights clearly. They effectively broke down the information, ensured comprehension at each stage, and articulated their thought process. They successfully bridged technical aspects with business concerns.</p>



<h3 class="wp-block-heading"><strong>Technical Methodology</strong></h3>



<p>Rating: Outstanding</p>



<p>The candidate showcased a comprehensive understanding of the required technical methodologies. They considered a range of data types and corresponding preprocessing steps, such as one-hot encoding for categorical variables and imputation for missing values. Their emphasis on avoiding label leakage during the data split and their caution against using PCA for its lack of interpretability further highlighted their depth of understanding.</p>



<h3 class="wp-block-heading"><strong>Problem Solving &amp; Critical Thinking</strong></h3>



<p>Rating: Outstanding</p>



<p>The candidate approached the problem in a structured manner. They sought to clarify any ambiguities, identified potential pitfalls like data imbalance and label leakage, and suggested appropriate techniques for addressing them. Their ability to foresee challenges and provide preemptive solutions, such as focusing on recall due to its importance in retention strategies, speaks to their critical thinking skills.</p>


SQL [McKinsey] Employee Attrition

[McKinsey] Employee Attrition

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