What Are Some Examples Of Data Science Job Interview Questions
1. A chit-chat question.
- Allows you to connect with your interviewer, talk about yourself, and things that you’ve done in the past.
- Your answer should be phrased to fit within the context of what the company is trying to achieve, to sell yourself and your ability to solve their problems.
Example: “Tell me an interesting technical problem that you solved.
- Explain how you approached a technical challenge you solved, what you took into consideration, what methodologies and modeling you used.
- Phrase your answer in a data science context. Talk about a project that is relevant to that company and the position to which youre applying.
2. Technical knowledge.
- Questions like this are meant as filter questions to determine whether or not your knowledge of data science is legitimate.
- You will be expected to answer quickly and accurately.
Example: Describe the differences between supervised and unsupervised learning.”
- It’s one of those benchmark questions if you don’t know it, the interviewer can tell.
- Your answer doesn’t have to be a textbook definition but you need to explain clearly what the differences are between the two types of learning, why one has certain characteristics, and the other has different characteristics.
- Mention experiences you’ve had working with one versus the other or both.
- Its important to prepare you don’t want to dance around a question like this.
List Down The Conditions For Overfitting And Underfitting
Overfitting: The model performs well only for the sample training data. If any new data is given as input to the model, it fails to provide any result. These conditions occur due to low bias and high variance in the model. Decision trees are more prone to overfitting.
Underfitting: Here, the model is so simple that it is not able to identify the correct relationship in the data, and hence it does not perform well even on the test data. This can happen due to high bias and low variance. Linear regression is more prone to Underfitting.
Questions To Ask Your Team To Help Develop A Job Spec
What Projects Is Your Data Science Team Working On
Like we have said a few times. Data science is a broad field .
Thus, the types of projects data scientists do are also broad. It applies to chat bots, deep learning, product recommendation, strategy development, new value streams, and so on! So what project is your company looking for a data science to do?
If the data science project requires terabytes of data that stream live, then you will need a data scientists that can manage Hadoop or some other multi-distributed system.
There is no way SQL can truly handle that data well. Trust us, we have seen companies wait days for queries to return because they were managing such large data warehouses.
If on the other hand, your company is managing data warehouses that have data-pulls occur infrequently, then hiring a data scientists with a SQL background will suffice.
From a coding perspective. We believe that data scientist should be fluent in either R or Python and at least aware of how to use the other. R is more of what we consider a research language. Even though SQL Server has recently integrated R into SQL Server 2016, its not at the level of system integration that Python has.
Python works so well everywhere, that when it comes to integrating code and models into larger systems. Python works great.
What Skills Are Needed On The Data Science Team?
This all depends on the data science project you would like to start.
Move Quickly When You Find The Right Candidate
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What If Youre Not Getting Data Science Job Interviews
If youve got the technical skills needed for the roles to which youre applying and youve done a good job preparing your application materials, eventually youre going to start hearing back from employers interested in interviewing you.
Dont be discouraged if this takes a while or if you have a low response rate. Thats quite common when applying to entry-level jobs. Theres a lot of competition for these positions, and the job hiring process can be fairly arbitrary. Stick with it!
If youre applying to hundreds of jobs and not getting any interviews, though, theres a good chance that something is wrong.
It could be that youre not qualified for the jobs youre applying to, or it could be that something in your application materials is bothering recruiters. Either way, it might be worth consulting with someone knowledgeable who can assess your application and give you some idea of whats going wrong.
Dataquest Premium subscribers can get personalized career advice from our trained Career Services community moderators. You can also ask friends or contacts in your data science network to help you figure out whats going wrong.
Once you do start getting interviews, though, youre going to want to be prepared for them. Your application materials have given you a shot, but the interview process is where you seal the deal and confirm to potential employers that you are the right person for the job.
How Do You Build A Random Forest Model
A random forest is built up of a number of decision trees. If you split the data into different packages and make a decision tree in each of the different groups of data, the random forest brings all those trees together.
Steps to build a random forest model:
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What Are The Components Of A Data Science Job Interview
Generally, the data science job interview involves three different steps:
1. A phone screening with a recruiter, someone from HR, or hiring manager.
- This is a conversation to get to know each other, to understand your background, and talk about projects you’ve done. Its also a chance for you as a candidate to understand more about the company, express your interest, and see why the company is a good fit for you.
- Usually 30 minutes to 1 hour long
- Could be a take-home challenge or a live coding challenge where someone watches you code.
- The language usually is your choice, but expect to code in Python or SQL.
- Companies sometimes give you a version of their data set for you to solve a problem. Eg: “Here is information about our users. Find the features that lead to increased viewership, write it out in code, present it to us.
- Time limits can vary from 1 to 2 hours for a live coding challenge or 24 to 48 hours for a take-home assignment, or they may not give you a limit. In general, no data challenge should take you more than 10 to 15 hours.
3. An onsite interview with a team lead, the data science manager, or a senior data scientist.
Pace To Prepare For A Data Science Interview
Here I am going to mention 6 steps to help you prepare and crack your data science interview. To improve your skills and follow these steps.
Pace 1
Before appearing in a data science interview, read the job roles or profile first, especially for skills, techniques, and tools. If the job description is not detailed enough, the research will be mentioned on the company website and will review what type of data science position is available there and what type of knowledge they expect from the candidate.
Most data science interview is a combination of aptitude, technical knowledge, and analytical thinking.
Pace 2
Dont forget to refresh your knowledge of relevant skills before the interview. To analysis your technical skills, the interviewer will commonly ask you about statistics, machine learning, and programming, etc. Make sure you brush up on languages like Python, R, and Tableau. The interviewer usually asks the programming question from these languages and checks your knowledge of these languages.
Pace 3
Improve your skills in some key topics such as:
- probability
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In Your Choice Of Language Write A Program That Prints The Numbers Ranging From One To 50
But for multiples of three, print “Fizz” instead of the number, and for the multiples of five, print “Buzz.” For numbers which are multiples of both three and five, print “FizzBuzz”
The code is shown below:
Note that the range mentioned is 51, which means zero to 50. However, the range asked in the question is one to 50. Therefore, in the above code, you can include the range as .
The output of the above code is as shown:
Pro Tip #: Be Ready For Open
Hiring managers and recruiters wont necessarily ask many academic or textbook questions. Instead, theyll likely present you with broad, real-world, open-ended questions, business problems, or case studies.
For example, Evan Butters, a data science recruiter at Wayfair, asks questions that are related to a challenge thats actually being worked on at the company and then assesses how the candidates would go about addressing it. This opens up a conversation and allows managers to see exactly how youd work as part of the actual team.
Open-ended questions allow candidates to demonstrate their problem-solving skills and require them to:
- Understand the problem. What exactly is the issue and what is the end goal that youre seeking to achieve? Why is this problem important to solve for this particular business and industry?
- Define the boundaries. In your answer, outline the scope of the problem and any assumptions youve undertaken in order to address it. For example, is the solution generalizable, or would it only work in this specific instance? Why?
- Discuss the trade-offs. Your ability to think through and articulate the pros and cons of different possible solutions is the most important part of your answer, Butters says, so weigh this heavily in your response.
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What Questions Can You Expect In A Data Scientist Interview
Nakamori, along with Abhinav Unnam and Benn Stancil suggest some questions you should expect to face in a data scientist job interview:
- Python coding test, which typically uses the concept of lists, dictionary, and so on:
- Find all combinations of strings in a specific URL consisting of strings meeting specific requirements.
- Schedule algorithms for total time spent via a series of overlapping time intervals. Take the union of the time.
Machine Learning And Ai Interview Questions
Machine learning and AI are important and fast-growing disciplines within data science. Using machine learning, we can now make more accurate, higher-value predictions at a faster pace than ever beforeall with minimal human intervention. So prepare for questions that probe your knowledge of this fast-growing field. For instance:
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Go On To The Next Episode
Okay, we went through a lot of typical data science job interview questions for juniors.This is not an exhaustive list but I hope that will make you more comfortable and more prepared for your next onsite interview.
If you nailed this part, you should be at the final stage Getting an offer. Which leads us to the next and final episode of this series: Negotiating the data science job salary.
Should I Sign An Invention Disclosure Agreement
Your prospective employer might ask you to disclose previous inventions codes, algorithms, software programs and models you may have written or contributed to and they may ask you to sign an agreement that gives full or partial ownership to them for any inventions you create during the time you are employed with them.
In such instances, you might want to consult with an employment attorney, your academic counselor, a professor or mentor for advice. These people might also be able to help you fully understand your legal rights when it comes to signing pre-invention agreements and property and inventions agreements that are conditions for hire.
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What Does A Data Scientist At Netflix Do
When it comes to filling out its ranks, Netflix is known for a few key practices: it almost exclusively hires senior-level professionals who have at least five years of experience, and it hires for ultra-specific roles to meet the needs of its teams. This means the responsibilities of a Netflix data scientist can greatly vary depending on the team theyve joinedsome might spend the bulk of their time working on personalization algorithms, while others might focus on product research and tooling, or teaming up with user interface designers and engineers to figure out how to optimize the user experience.
Regardless of the team, though, Netflixs data scientists can be sure that their contributions have a meaningful impact on Netflixs products and services. As a growing number of entertainment companies invest in streaming, the demand for data scientists and analysts has grown alongside it, and nowhere is this more apparent than at Netflix.
You do not make a $100 million investment these days without an awful lot of analytics, Dave Hasting, Netflixs direction of product analytics, said in 2015.
What Are Your Favorite Resources To Prepare For A Data Science Job Interview
- Gives an in-depth overview of how to work with SQL databases, which oftentimes is a focus on data science interviews. The majority of data science jobs require SQL knowledge so ModeAnalytics is a fantastic resource.
- A gamified version of different quizzes and questions that have been asked at companies in real data science interviews at companies like Google and Facebook. You get a comprehensive overview of what’s going on, plus it covers all the bases of data science machine learning.
3. Chris Albon
- Chris Albon is very well known in the data science community and is a great resource for entry-level data scientists. His website has a never-ending list of materials which cover machine learning, linear algebra, and statistical modeling of various types. He also has handy machine learning flashcards.
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Research The Role And Identify Your Fit
Read the entire job description thoroughly, and consider what the responsibility and tasks youâll be performing are. From there, you can gauge the soft and technical skills that youâll need for the job. To really nail the interview and prepare properly, youâll need to have a clear idea of what the role is and what the requirements will be.
Look up what the interviewer does at the company in most cases, the main interviewer will be an immediate â or close â supervisor for the position you are applying to. Researching them, their role, and critically thinking about how your roles will interact will be helpful during the interview .
With a clear idea of what the role and job description are, youâll be able to predict which topics will be covered in the interview, and better determine which topics to focus on when preparing. If you havenât performed a relevant task since since you last left school, you may want to brush up on it before the interview so you know how to discuss it with confidence.
Itâs also important to research industry, company, and technical terminology so you sound informed, can follow along, and can engage throughout the interview.
What Is The Interview Process For A Data Scientist At Netflix Like
Similar to other technology companies, the data science job interview process at Netflix begins with an online application. Candidates whose résumés impress will then move onto a phone screening with a hiring manager who will ask both HR-type and technical interview questions, before doing onsite interviews at Netflixs offices in either Los Gatos, CA, or Los Angeles.
- The phone screener
Data scientists who have interviewed with Netflix report that the initial phone screener can be heavy on technical questions and that the companys recruiters like to dig deep into a candidates prior projects and experience. Expect specific questions about why you chose a certain algorithm for a project, or how you built a certain machine learning or analytics system.
- The culture dek
Netflix stresses the importance of its company culture, so candidates should be prepared to talk about how they align with that culture. Candidates should also come ready to talk about how prior experiences and projects demonstrate their commitment to Netflixs values such as independent decision-making, curiosity, clear communication, selflessness, and innovation. Data scientists who have interviewed with Netflix strongly recommend reading about Netflixs culture ahead of the interview.
- The in-person interview
The Netflix interview loop for data scientists consists of interviews with six or seven hiring managers, data scientists, engineers, and executives.
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