Let’s say you’re interested in a career in data science, and you manage to land a job interview at a firm that wants your data-crunching expertise. What kind of questions will you face? What sort of skills and abilities should you emphasize in your answers?

For those unaware, a data scientist is responsible for understanding and aggregating datasets, and employing statistical and machine learning techniques to create predictive analytics and models. They work with data and application engineers to integrate these models into a software platform or product; alternatively, they use the data to help identify opportunities to improve organizational efficiency and increase business value.

The field of data science is broad, and if you’re sitting across the desk from a potential employer, it’s important to know exactly what the company is looking for, and how your skill set as a data scientist is going to add value to the organization.

Steve Donahue, principal recruiter at Entrust Datacard, said data science candidates should consider themselves like a puzzle piece. How do you fit into the “puzzle” of the company where you’re interviewing?

As he noted, not every company uses the same tools, and you may bring something to the organization that they didn’t even know they needed. Considering that, it’s important to own what you have done and be proud of it.

“When we recruit a data scientist, we’re looking for experience with specific statistical modeling tools such as R, SciKitLearn or TensorFlow,” Donahue said. “For technical roles, we like to get into the nuts and bolts of the candidates’ previous roles.”

If you’re interviewing for a data science role, you must prepare for those kinds of technical questions, as well as your previous experience. For example:

• How you can build a predictive model in the absence of labeled data (using unsupervised ML techniques, or keyword-based approaches to generate labels)?

• What did you do daily? What did you do on the team?

On a broader level, data scientists are adept at problem-solving. They use statistics, machine learning algorithms, and data visualization techniques to identify trends, patterns, and correlations in massive sets of data.   

Insights generated by data scientists can significantly impact company decision-making. Data scientists help inform leadership to help them identify where the company should be headed, and how it can improve existing operations. Predictive models help forecast future trends and identify risks that several teams may find useful, too. In that spirit, many job interview questions will also focus on the impact of your analysis on your previous employers.

Here are some other sample questions you might face:

Sample question: "Explain the difference between supervised and unsupervised learning and provide an example of each. How would you approach a problem using each method?"

Sample answer: This question assesses a candidate's understanding of fundamental machine learning concepts and their ability to apply them to real-world scenarios.

Supervised learning involves training a model on a labeled dataset, where each data point has an associated output or target variable. The model learns to predict the output for new, unseen data. Examples include:

• Regression: Predicting a continuous numerical value (e.g., predicting house prices).

• Classification: Predicting a categorical value (e.g., classifying emails as spam or not spam).

Unsupervised learning involves training a model on an unlabeled dataset, where the model must identify patterns or structures within the data without explicit guidance. Examples include:

• Clustering: Grouping similar data points together (e.g., customer segmentation).

• Dimensionality reduction: Reducing the number of features in a dataset while preserving important information (e.g., principal component analysis).  

Here’s an ideal approach:

• Supervised learning:
  1. Data preparation: Clean and preprocess the data, handling missing values, outliers, and feature engineering.
  2. Model selection: Choose appropriate algorithms based on the problem type (regression or classification) and dataset characteristics.
  3. Training: Train the model on the labeled dataset.
  4. Evaluation: Assess the model's performance using metrics like accuracy, precision, recall, or mean squared error.
  5. Tuning: Fine-tune the model's hyperparameters to improve performance.
• Unsupervised learning:
  1. Data preparation: Like supervised learning, but without the need for labels.
  2. Algorithm selection: Choose algorithms based on the desired outcome (clustering, dimensionality reduction, etc.).
  3. Training: Apply the algorithm to the unlabeled dataset.
  4. Interpretation: Analyze the results to gain insights into the data structure.

This answer demonstrates a solid understanding of the key differences between supervised and unsupervised learning, along with the ability to apply these concepts to real-world problems. It also highlights the candidate's knowledge of data preparation, model selection, training, evaluation, and tuning, which are essential skills for a data scientist.

Data Scientist Anmolika Singh tells Dice that candidates should give a holistic view of their skillset, as well: “Three technical skills—programming, machine learning, and statistics—are essential for a data scientist, enabling them to transform raw data into meaningful, actionable outcomes. I like to think of a data science projects as building of a house—input, processing, and output.

“The input is like gathering raw materials—this is where programming proficiency comes in. Python, R, and SQL are the tools that help you take the raw data and shape it into something workable. Then comes the process, which is all about designing the blueprint. This is where machine learning algorithms come into play—using techniques like linear regression or neural networks to create and refine the model. Finally, statistical knowledge is the finishing work—the paint and details that turn a structure into a livable space. This knowledge helps you interpret the model’s results, understand the data, and deliver actionable insights.”

Sample question: "Explain the key differences between machine learning and deep learning. Provide examples of when you would choose one over the other."

“Machine learning (ML) is a subset of AI focused on algorithms that extract patterns from data, while deep learning (DL) is a subset of ML where neural networks with multiple layers (deep networks) are employed,” Singh explains. “ML often requires manual feature extraction, whereas DL automates this process by going through layers of abstraction to unveil complex patterns.”

This question assesses the candidate's understanding of fundamental machine learning concepts and their ability to distinguish between traditional machine learning and deep learning approaches. Here’s a deeper breakdown:

Machine learning is a broader field that encompasses algorithms and techniques for building models that can learn from data. It involves extracting patterns and insights from data to make predictions or decisions. Traditional machine learning methods often rely on handcrafted features and require human intervention to engineer these features.

Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers. These neural networks can learn complex representations directly from raw data, eliminating the need for extensive feature engineering. Deep learning models excel at tasks that involve processing large amounts of unstructured data, such as images, audio, and text.

Neural networks are computational models inspired by the human brain. They consist of interconnected nodes (neurons) organized in layers. Each neuron processes information and passes it on to the next layer. Deep learning models typically have many layers, allowing them to learn hierarchical representations of data.  

Choosing between machine learning and deep learning:

• Data availability and quantity: Deep learning often requires large datasets to train effectively. If you have limited data, traditional machine learning methods might be more suitable.
• Task complexity: Deep learning is well-suited for complex tasks like image recognition, natural language processing, and speech recognition. For simpler tasks, traditional machine learning algorithms might be sufficient.
• Feature engineering: Deep learning can automatically learn features from raw data, reducing the need for extensive feature engineering. However, if you have domain expertise and can engineer informative features, traditional machine learning methods can be effective.
• Computational resources: Deep learning models can be computationally expensive to train, especially for large datasets. Consider the available hardware and computational resources before choosing a deep learning approach.

Sample question: "Explain the difference between RMSE (Root Mean Squared Error) and MSE (Mean Squared Error) in the context of linear regression. When would you choose one over the other?"

RMSE (Root Mean Squared Error) and MSE (Mean Squared Error) are both metrics used to evaluate the performance of linear regression models. They measure the average difference between the predicted values and the actual values.

• MSE calculates the average squared difference between the predicted and actual values. It penalizes large errors more severely than small errors.
• RMSE is the square root of the MSE. It returns the error in the same units as the target variable, making it easier to interpret the results in a meaningful way.

Choosing between RMSE and MSE:

• Interpretability: RMSE is often preferred because it is in the same units as the target variable, making it easier to understand the magnitude of the errors.
• Minimization: Both RMSE and MSE are often minimized during the training process. However, RMSE can be more sensitive to outliers due to the squaring operation.
• Specific use cases: In some cases, the choice between RMSE and MSE may depend on the specific application and the desired interpretation of the results. For example, if you are interested in minimizing the average absolute error, you might consider using MAE (Mean Absolute Error) instead.

In general, RMSE is a good default choice for evaluating the performance of linear regression models due to its interpretability and widespread use. However, it is important to consider the specific context of the problem and the desired interpretation of the results when selecting a metric.

Singh adds: “MSE (Mean Squared Error) measures the average squared differences between predicted and actual values, magnifying larger errors. RMSE (Root Mean Squared Error) is simply the square root of MSE, making it more digestible in the same units as the predicted variable. Both metrics evaluate model performance by penalizing errors.”

Sample question: "Describe a real-world scenario where data cleaning and preparation were crucial. What steps did you take to ensure data quality?"

Sample answer: “I worked on a project to predict customer churn using a large dataset of customer information. The dataset contained a variety of features, including demographic information, purchase history, and customer service interactions.

“Data cleaning and preparation steps:

• Handling missing values: We identified missing values in several columns, such as customer income and purchase frequency. We imputed missing values using appropriate techniques, considering the nature of the data and the potential impact on the model. For example, we used the median for numerical variables and the most frequent category for categorical variables.
• Dealing with outliers: We detected outliers in some numerical features, such as customer age and purchase amounts. We analyzed the outliers to determine if they were valid data points or errors. In cases where outliers were deemed invalid, we removed them or corrected them.
• Data normalization: We normalized numerical features to a common scale, ensuring that features with larger magnitudes did not dominate the model. This helped to prevent bias and improve model performance.
• Feature engineering: We created new features that might be more informative for predicting customer churn, such as customer tenure and average purchase value.
• Data consistency: We checked for inconsistencies in the data, such as duplicate records or conflicting values. We resolved these inconsistencies to ensure data accuracy.

“By carefully addressing these data quality issues, we were able to improve the reliability and accuracy of our customer churn prediction model.”

Sample question: "Describe a data science project where you used Python. What specific Python libraries or packages were essential for your work, and why?"

Sample answer: “I worked on a project to develop a recommendation system for a streaming platform for books. The goal was to predict which books users would be interested in based on their viewing history.”

Potential Python libraries and packages to mention:

• Pandas: For data manipulation and analysis. Pandas provided efficient data structures and functions for cleaning, transforming, and exploring the dataset.
• NumPy: For numerical computations and array operations. NumPy's powerful array objects and mathematical functions were essential for implementing machine learning algorithms and performing calculations.
• Scikit-learn: For machine learning algorithms. Scikit-learn offered a comprehensive collection of algorithms, including collaborative filtering, which is commonly used for recommendation systems.
• Matplotlib and Seaborn: For data visualization. These libraries helped us visualize the data, identify patterns, and gain insights into user behavior.

Why these libraries were essential:

• Pandas and NumPy: These libraries provided the foundation for data analysis and manipulation, allowing us to efficiently work with large datasets and perform calculations.
• Scikit-learn: The library's implementation of collaborative filtering algorithms simplified the process of building the recommendation system.
• Matplotlib and Seaborn: Visualization was crucial for understanding the data and evaluating the performance of the recommendation system.

Openness About Data Science Challenges

During the interview, t’s important to discuss the problems and challenges (and there are many) that inevitably come with data science, especially when wrestling with huge and messy datasets. “It’s also said it’s important to know how a candidate handles challenges as well as success—we all know that not everything goes as planned in development,” Donahue added.

For example:

• What do you do when your project is failing?

• What are some alternative approaches or alternative statistical models you could make?

With that in mind, walk into the interview with a few stories of how you confronted (and overcame) a data-science challenge.

Ideally, the interviewer’s questions will be designed to reflect the nature of the work you’ll be performing at the company, and the kinds of data you will be dealing with. If the organization wants someone who can comb through some extraordinarily messy, unstructured datasets for strategic insights, for example, you might want to talk about how you dealt with something similarly messy at your old firm.

In addition to simply answering questions, you may be asked to demonstrate your skills. For example, you may be given a file containing mock data about traffic to different landing pages of your website and asked to build a model that predicts conversion rates. It’s worth reviewing how to best use your tools and techniques before heading into the interview, especially if you know a test might be coming.

Radu Miclaus, director of product AI and cloud at Lucidworks, said more than any solution itself, interviewers are looking to see if you ask clarifying questions about the data, state the assumptions you’re making, and explain your thought process as you work through the problem. In other words, your thought process counts just as much as the solution.

“When we hire, we have to find the sweet spot between technology skills and business acumen,” he said. “We’re looking for people who can solve business problems, so we’re interested in how they screen the data and present the results. Understanding the methodology, you’d go through, the process of how they think through the code, how they document the code.”

Questions that might relate to this include:

• What are some of the data sources you would use?

• How are you going to acquire data, and what’s the end goal?

• How do you structure the entire project around those considerations before adopting a methodology?

• Can you take the problem back and think about it more holistically?

“Data scientists have to operate in an agile fashion—they have to think about version control, understanding the process of organization for maintaining code, and for documentation,” Miclaus said. “Data scientists are not decision-makers; they are providers of insight. It’s not enough for you to produce a model; it must be deployable and implantable.”

So, going into an interview, it’s crucial that any data-science candidate understand the backdrop of the methodologies mentioned in the job posting, as well as how those are applied to achieve business goals. For example, if the company is focused on mapping, or the Internet of Things (IoT), the candidate will absolutely need to know how methodologies apply to those segments.

And it’s not enough to walk into the data science interview with some generalized data-science concepts; you need to “lean in” to whatever specialization the company is asking for. If that specialization is already your area, that’s great! Otherwise, you may have a lot of homework before the interview itself.

Data Scientist Soft Skills

Finally, job candidates in data science can expect to field questions related to their commutation’s abilities and teamwork skills, Donahue said. As much as data scientists are prized for their numbers-crunching (and predictive) abilities, they also need the “soft skills” that will allow them to work in teams and communicate relevant results to the proper stakeholders.

“I ask how and when they ask for help,” Donahue added. “This shows how us they think and work as part of a team and take advantage of people around them who can point them in the right direction toward solutions. When working on highly successful teams, splits on the direction of a project are going to happen.”

Here are some examples of “soft skill” interview questions:

• How do you get all team members on the same page?

• Do you have experience working in cross-functional teams?

• Do you understand collaborative processes?

• Do you understand what ‘Agile’ means?

Data science is a hot field right now, with employers hungry for technologists with the right combination of data-crunching and strategy-predicting skills. According to data from Glassdoor, “data scientist” is the highest-paying entry-level job in the U.S. this year, with a median base data scientist salary of $95,000. But to land the job—and earn that kind of salary—data scientists will need to demonstrate that they can get the job done, as well as the soft skills to interact with teammates and sell their conclusions to their broader organization. A great way to showcase your skills is through data science certifications.

Conclusion

Practice answering known data scientist interview questions ahead of your interview as preparation for some of the common technical queries you’re likely to get. Early in the technical interview process, the hiring team will want to know you have a firm grasp on data science basics. Knowing which questions and answers will push you forward in the interview process is key.

Later, you’ll have to demonstrate your problem solving and reasoning capabilities. Technical skills are only one part of the data scientist’s day-to-day work. Being able to communicate those findings also matters.