Data Scientists learn from large datasets by programming predictive models and building machine learning algorithms to achieve a particular set of business needs. Their ultimate goal is to discover trends and patterns in large amounts of information to develop data-driven solutions for an organization. Their work requires them to identify relevant datasets and automate data collection, clean and process large amounts of structured and unstructured data, and build data models to analyze the data and search for trends. Data scientists are typically also responsible for presenting their findings to relevant stakeholders and using those insights to propose solutions or strategies to help the business achieve its objectives. They also usually work closely with engineering teams and product development teams when in technology companies to define their goals and develop solutions.
+
Quantitative Data Analysis
+
Critical Thinking
+
Attention to Detail
+
Data Presentation
+
Communication
+
Data Wrangling
How to effectively interview Data Scientists
While it might seem difficult to figure out whether a candidate will succeed as a Data Scientist in your company, a well-developed set of interview questions that tap into the core skills required to perform in a Data Scientist role will go a long way in helping you decide.
But where do you start? How do you develop a set of great interview questions?
The best interview questions come directly from a job analysis. A job analysis is an evidence-based method that focuses on assessing key features of a particular role. These features describe both the job itself (i.e., tasks, responsibilities, and performance objectives), and the characteristics required of someone to perform successfully in the job (e.g., knowledge, skills, and abilities). A job analysis forms the basis of many HR practices such as compensation, performance management, and - you guessed it - how to interview and hire candidates.
At Hireguide, we’ve done the job analysis work for you. We’ve used the method to identify a core set of skills associated with the Data Scientist role, and we’ve developed and validated a list of behavioral and situational questions with answer guides that tap directly into those core skills. And that’s not all. We’ve compiled these questions and created a Data Scientist Interview Template for you that integrates other interviewing best practices. Skills-based interviews will not only help you make higher quality and evidence-based hiring decisions, but research also shows they enhance fairness and reduce bias in your hiring process.
Example questions to ask
Question 1
Imagine another team is having difficulty understanding complex insights you gathered from a predictive algorithm or data model. What would you do to present the data in a more accessible way?
Answer Tips
- Suggests using visualization methods (e.g. charts and infographics)
- Shares tips for explaining complex analyses (e.g. focus on relevant outcomes)
- Considers storytelling when framing findings/insights
- Emphasizes the importance of effectively communicating findings to different audiences
- Discusses customizing visualizations/message based on audience and purpose
Question 2
Tell me about a time where you were working with a complex database that was constantly evolving. How did you manage that data to effectively achieve your objectives for the project/company?
Answer Tips
- Considers machine learning or automating processes to deal with constantly evolving data
- Discusses various analytic methods (prediction, classification, association)
- Explains and justifies their selected approach by considering the pros/cons
- Explains how their solution helped achieve
