Objective
The following case study allows interviewers to explore how data science candidates consider a problem, and the types of questions they may ask to create the best possible model output. The questions for junior data scientists focus on their understanding of different models, technical considerations in data preprocessing, and basic evaluation metrics. The questions for senior data scientists explore their expertise in handling complex data scenarios, advanced modeling techniques, interpretation and communication of results, and the ability to provide actionable insights to guide business decisions.
Background:
A company's customer service department wants to develop a predictive model to forecast call volumes accurately. They have enlisted the expertise of a data scientist to create a model that can help them optimize staffing and resources. The data scientist's role is to analyze the available data, identify suitable predictive models, and provide insights into the forecasted call volumes.
Project Scope:
The project involves the following key objectives:
1. Data Analysis:
- Analyze historical call volume data, taking into account relevant factors such as time of day, day of the week, seasonality, and any external events that may impact call volumes.
- Identify trends, patterns, and correlations in the data that can help understand call volume fluctuations.
2. Model Selection:
- Identify suitable predictive modeling techniques for forecasting call volumes.
- Evaluate different types of models such as time series models, regression models, or machine learning algorithms, considering their strengths and limitations.
3. Model Development:
- Preprocess the data by handling missing values, outliers, and other data quality issues.
- Split the data into training and testing sets to develop and evaluate the predictive models.
- Develop, train, and tune the selected model(s) using appropriate techniques.
4. Model Evaluation and Interpretation:
- Assess the accuracy and performance of the developed model(s) using appropriate evaluation metrics such as mean absolute error (MAE) or root mean squared error (RMSE).
- Interpret the model results and provide insights into the factors that influence call volumes.
- Explain the meaning and implications of the forecasted call volumes for resource planning and staffing decisions.
5. Documentation and Communication:
- Document the data preprocessing, model development, and evaluation processes.
- Communicate the findings, limitations, and recommendations to stakeholders in a clear and understandable manner.
- Provide guidance on how to interpret and utilize the forecasted call volumes effectively.
Example Output of a Forecasting Model:
The developed forecasting model predicts call volumes for the next week based on historical data and relevant factors. Here is an example output for interpretation:
- Week 1:
- Forecasted Call Volume: 500 calls
- Actual Call Volume: 480 calls
- Error (MAE): 20 calls (4% deviation)
Open Interpretation:
Given the performance above, summarize the model accuracy and its implications to a group of business stakeholders interested in its overall accuracy and trustworthiness.
Example Response: The model accurately predicted a call volume of 500 calls for Week 1, with a small deviation of 20 calls (4%) from the actual call volume of 480 calls. This suggests that the model is performing reasonably well in forecasting call volumes. It indicates that staffing and resource planning should be aligned with an anticipated call volume of around 500 calls for the upcoming week.
Key Questions for Junior Data Scientists:
1. What are some commonly used predictive modeling techniques for forecasting call volumes?
2. Can you explain the differences between time series models, regression models, and machine learning algorithms for this forecasting task?
3. What are the key technical considerations when handling missing values and outliers in the call volume dataset?
4. How would you split the call volume data into training and testing sets, and why is this important in model development?
5. Which evaluation metrics would you use to assess the performance of the predictive model(s) for call volume forecasting, and what do they indicate?
Key Questions for Senior Data Scientists:
1. How would you assess the seasonality and trends in the historical call volume data, and how would you incorporate these factors into the forecasting model?
2. Can you describe a scenario where ensemble modeling techniques could be beneficial for forecasting call volumes, and how would you implement them?
3. How would you address the challenge of handling high-dimensional data or incorporating additional external factors that may impact call volumes?
4. What methods or techniques would you
employ to interpret and explain the results of the forecasted call volumes to stakeholders, highlighting the key factors driving the forecasts?
5. Given an example forecasted call volume model, how would you interpret the meaning of its coefficients or features and provide actionable insights to guide resource planning and staffing decisions?