5 answers
Qinglin
Student
Pittsburgh, Pennsylvania
4
Questions
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What aspects do data science job interviews typically cover?
I am graduating by the end of this year and have started building my resume and preparing for job interviews. I wonder if there is a "leet code" equivalence in the ds field that I should be working on? What types of questions do a data science job interview typically includes? Thank you in advance for your answer :D
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5 answers
Micheal White
Reimbursement and Quality Metrics Management
18
Answers
Panama City Beach, Florida
Updated
Micheal’s Answer
Hello, congratulations for almost being finished. When I did the MIT data science program which is a field I use but currently don't work in specifically they prepared us for questions like what is PCA how's it used? K means k. Mediodes etc etc. They also told us that you would have scenario questions. What to do if you're underfitting overfitting a model etc. The important thing is and this they stressed quite exuberantly is be prepared. But if you don't know the answer, walk them through how you would find it. I would also encourage you, if you haven't already, start building your portfolio doing some independent projects through kaggle or GitHub when except any internship opportunities you can find. I hope that helps.
Thank you so much Michael! This helps a lot! I have some initial ideas for the questions you mentioned, but I may have to prepare the answers more thoroughly.
Qinglin
Hetal Salunia
AWS Solution Architect
7
Answers
Piscataway Township, New Jersey
Updated
Hetal’s Answer
Data science job interviews can cover a wide range of topics, depending on the company and the specific role. However, here are some common aspects that data science job interviews typically cover:
Technical Skills: The interviewer may ask questions about technical skills related to data science, such as programming languages (e.g., Python, R), statistical methods, data analysis, and machine learning algorithms.
Data Wrangling: The interviewer may ask about data wrangling techniques, including data cleaning, data transformation, and data integration.
Domain Knowledge: The interviewer may ask questions about the industry or domain the company operates in, such as finance, healthcare, or e-commerce. This helps to ensure that the candidate has an understanding of the business context in which they will be working.
Problem-Solving: The interviewer may give the candidate a data-related problem to solve and ask them to walk through their thought process and approach to solving it.
Communication Skills: Data scientists are often required to communicate complex technical information to non-technical stakeholders. Therefore, the interviewer may ask questions to assess the candidate's communication skills, such as how they would explain a technical concept to a non-technical audience.
Cultural Fit: Companies often prioritize candidates who fit well with their culture and values. The interviewer may ask questions about the candidate's work style, team collaboration, and problem-solving approach to assess their fit with the company.
These are just some of the aspects that data science job interviews typically cover. It's important to research the company and the specific role in advance to get a sense of what to expect and prepare accordingly
Technical Skills: The interviewer may ask questions about technical skills related to data science, such as programming languages (e.g., Python, R), statistical methods, data analysis, and machine learning algorithms.
Data Wrangling: The interviewer may ask about data wrangling techniques, including data cleaning, data transformation, and data integration.
Domain Knowledge: The interviewer may ask questions about the industry or domain the company operates in, such as finance, healthcare, or e-commerce. This helps to ensure that the candidate has an understanding of the business context in which they will be working.
Problem-Solving: The interviewer may give the candidate a data-related problem to solve and ask them to walk through their thought process and approach to solving it.
Communication Skills: Data scientists are often required to communicate complex technical information to non-technical stakeholders. Therefore, the interviewer may ask questions to assess the candidate's communication skills, such as how they would explain a technical concept to a non-technical audience.
Cultural Fit: Companies often prioritize candidates who fit well with their culture and values. The interviewer may ask questions about the candidate's work style, team collaboration, and problem-solving approach to assess their fit with the company.
These are just some of the aspects that data science job interviews typically cover. It's important to research the company and the specific role in advance to get a sense of what to expect and prepare accordingly
Hi Hetal, thank you so much for this detailed list. It is so helpful! I will keep those in mind and prepare accordingly.
Qinglin
Alex Ilin
Data Scientist at Facebook
8
Answers
Mountain View, California
Updated
Alex’s Answer
A lot depends on the specific role and DS archetype that the company is looking for. Analytics role will have more case studies and product questions while ML-oriented roles will have modeling. Both will have stats, A/B testing and data investigations.
There's a good book, "Ace the Data Science Interview", that covers a lot of topics and questions for a typical DS interview. Good luck!
There's a good book, "Ace the Data Science Interview", that covers a lot of topics and questions for a typical DS interview. Good luck!
Hi Alex, thank you for recommending!
Qinglin
Adit Bhiday
Software Engineer
11
Answers
Gibson City, Illinois
Updated
Adit’s Answer
Data science job interviews can cover a wide range of topics and skills, but some of the most common aspects that are typically covered include:
Technical skills: Interviewers may ask questions related to coding, programming languages, data manipulation, and statistical analysis. Candidates may be asked to write code or solve problems related to data cleaning, transformation, or analysis.
Machine learning: Data science positions often require a strong understanding of machine learning techniques and algorithms. Interviewers may ask questions related to model selection, optimization, and evaluation, as well as questions related to specific machine learning libraries and frameworks.
Statistics: Data science positions require a strong foundation in statistics, including knowledge of probability theory, statistical inference, and hypothesis testing. Interviewers may ask questions related to statistical modeling, experimental design, and data visualization.
Data analysis: Data scientists must be able to analyze data to extract insights and make decisions. Interviewers may ask questions related to exploratory data analysis, data visualization, and data interpretation.
Communication: Data scientists must be able to effectively communicate their findings to both technical and non-technical stakeholders. Interviewers may ask questions related to how candidates have communicated complex technical concepts to a non-technical audience.
Business acumen: Data science positions often require a strong understanding of the business context and how data science can contribute to business goals. Interviewers may ask questions related to how candidates have used data science to solve business problems or how they would approach a hypothetical business problem.
Overall, data science job interviews may cover a wide range of technical and non-technical topics, depending on the specific job requirements and the organization conducting the interview.
Technical skills: Interviewers may ask questions related to coding, programming languages, data manipulation, and statistical analysis. Candidates may be asked to write code or solve problems related to data cleaning, transformation, or analysis.
Machine learning: Data science positions often require a strong understanding of machine learning techniques and algorithms. Interviewers may ask questions related to model selection, optimization, and evaluation, as well as questions related to specific machine learning libraries and frameworks.
Statistics: Data science positions require a strong foundation in statistics, including knowledge of probability theory, statistical inference, and hypothesis testing. Interviewers may ask questions related to statistical modeling, experimental design, and data visualization.
Data analysis: Data scientists must be able to analyze data to extract insights and make decisions. Interviewers may ask questions related to exploratory data analysis, data visualization, and data interpretation.
Communication: Data scientists must be able to effectively communicate their findings to both technical and non-technical stakeholders. Interviewers may ask questions related to how candidates have communicated complex technical concepts to a non-technical audience.
Business acumen: Data science positions often require a strong understanding of the business context and how data science can contribute to business goals. Interviewers may ask questions related to how candidates have used data science to solve business problems or how they would approach a hypothetical business problem.
Overall, data science job interviews may cover a wide range of technical and non-technical topics, depending on the specific job requirements and the organization conducting the interview.
jie peng
financial services
3
Answers
Roselle, Illinois
Updated
jie’s Answer
Congratulations on nearing the (interim) end of your learning journey! LeetCode is a great resource to prepare for data science coding interviews. High-tech companies usually conduct rigorous coding interviews in addition to behavior-oriented ones. Interviews in traditional sectors may not be very technical, as it depends on the hiring manager's preference. Since different companies and even different teams within the same company have very different business focuses, it is highly recommended to check job websites such as Glassdoor, Blind, Levels.fyi, 1point3acres, etc. not only for prospective employers in general, but also nailing for interview posts/questions related to the job title/team/function you are applying for. Good luck!
Hello jie, thank you so much for the advice! :D
Qinglin