“That can cover statistics, machine learning, and math or other engineering areas. “Baseline technical skills are a must-have,” explains Feder. That last part is what makes the data science interview process different than other engineering roles. Data Science Technical Interviewĭuring technical interviews, companies are looking for two skills: technical skill and the ability to communicate insights. Let’s look at data scientist interview stages. The process usually includes a recruiter call, technical interview (which can include an asynchronous technical assessment, a live interview, or both), and a behavioral interview, though not always in that order. Though roles vary, most data science interviews have similar components. What’s included in a data science interview? What skills are absolutely required? What are nice-to-have? In hiring for this specific role, what business needs might the team be trying to address? Is the team hiring urgently? If there’s anything that would prevent you from taking the job, such as location, clarify exact requirements with the recruiter. “It’s getting popular as a way to differentiate between statistics and machine learning.” ![]() “A lot of companies now are using the ML engineer to refer to what a few years ago would have been called a data scientist: They’re building and testing models and deploying software to test those models,” Feder says. Specialized roles that focus on machine learning (ML), artificial intelligence (AI), or natural language processing (NLP) usually list those in the job title.Those questions will be more about building out pipelines or managing large data sets.” Data Engineer: Less about statistics and insights, and more about managing data sets and building pipelines: “At a high level, if you see a data engineer posting, that’s an engineering-heavy role: You won’t need stats or math as much, and it’s building out a pipeline or managing data.Data Scientist: Similar to the above, but with more tools, advanced mathematical models, and sometimes multiple data sources.Data Analyst: Gathers, cleans, and preps data and then gleans business insights.Here are some of the most common ones, with commentary from Feder: ![]() ![]() In the way that “cook” can refer to a local sushi chef, a Food Network host, or the person at your high school cafeteria, “data scientist” can mean very different things in different contexts.įeder points out that for all positions, “The best way to prep is to read the job description and talk to the recruiter.” Look at the information they’ve already shared with you and mine all available resources for insights, just like you would with a data set. But within this wide-ranging, rapidly-changing field, the same terminology can describe many different kinds of roles. How can you crush your data science interview? Mine the data in the job descriptionĭata science generally refers to the ability to use statistics, algorithms, and other tools to extract insights from data and interpret them for a wide audience. ![]() He has interviewed 250 data scientists for entry-level to senior managerial roles, so it’s an understatement to say he really knows data science interview questions. In a career spanning over a decade, he’s managed large data science teams and once did statistical analysis for the New York Yankees. įor more on what to expect from the interview process-and how to ace answering common data scientist interview questions-we’ve consulted data scientist Eric Feder. You might change your life, too.Īnd with GlassDoor ranking data science as #3 on its most recent list of Best Jobs in America, the demand for data scientists is only expected to rise. After weeks of applying for data science jobs, you get that email you’ve been waiting for: A recruiter has invited you to interview for the role! And if you can get through the interview process for what the Harvard Business Review called the sexiest job of the 21st century, you won’t only change your career.
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