Recently, our team of data science consultants had an awesome opportunity to present to a class of future data scientist at Galvanize Seattle. It was a lot of fun and we met a lot of ex-software developers and IT specialists. One student who had come to hear our talk was named Rebecca Njeri. She did not have a background in software engineering. However, she was clearly well adapted to the new world. In fact, for one of her projects she used company data to create a recidivism prediction model among former inmates using supervised learning models.
We love the fact that that her project was not just technically challenging, but that it was geared towards a bigger purpose than selling toasters or keeping customers from quitting your telecommunication plan! She also brought up her experience interviewing for data science roles at Microsoft and other large corporations and how it taught her so much. We wanted to share what she learned so we asked if she would write us a guest post! And she said yes! So without further ado, here is
How to Prepare for a Data Science Interview:
If you are here, you probably already have a Data Science interview scheduled and are looking for tips on how to prepare so you can crush it. If that’s the case, congratulations on getting past the first two stages of the recruitment pipeline. You have submitted an application and your resume, and perhaps done a take home test. You’ve been offered an interview and you want to make sure you go in ready to blow the minds of your interviewers and walk away with a job offer. Below are tips to help you prepare for your technical phone screens and on-site interviews.
Read the Job Description for the Particular Position You are Interviewing for
Data Scientist roles are still pretty new and the responsibilities vary wildly across industries and across companies. Look at the skills required and the responsibilities for the particular position you are applying for. Make sure that the majority of these are skills that you have, or are willing to learn. For example, if you know Python, you could easily learn R if that’s the language Data Scientists at Company X use. Do you care for web-scraping and inspecting web pages to write web-crawlers? Does analyzing text using different nlp modules excite you? Do you mostly want to write queries to pull dataca from SQL and NoSQL databases and analyse/build models based on this data? Set yourself up for success by leveraging your strengths and interests.
Review your Resume before each Stage of the Interviewing Process
Most interviews will start with questions about your background and how that qualifies you for the position. Having these things at the tip of your fingers will allow you allow you to ease into the interview calmly as you won't be fumbling for answers. Use this time to calm your nerves before the technical questions begin.
Additionally, review your projects and be prepared to talk about the Data Science process you used to design your project. Think about why you chose the tools that you used, the challenges that you encountered along the way, and the things that you learned along the way.
Look at GlassDoor for Past Interview Questions
If you are interviewing for a Data Scientist role at one of the bigger companies, chances are they’ve already interviewed other people before you, who may have shared these questions on GlassDoor. Read them, solve them, get a feel of the questions you will be asked. If you cannot find previous questions for a particular company, solve the data science questions from other companies. They are similar, or at the very least, correlated.
Moreover, even if there are no data science questions for that particular company, see what kind of behavioral questions are asked.
Ask the Recruiter about the Structure of the Interview
Recruiters are often your point of contact with the company you are interviewing at. Ask the recruiter questions about how your interview will be structured, what resources you should use when preparing for your interview, what you should wear to the interview, and even the names of your interviewers so you can stalk look them up on LinkedIn and see their areas of specialization.
Do Mock Interviews
Interviewing can be nerve-racking, more so when you have to whiteboard technical questions. If possible, ask for mock interviews from people who have been through the process before so you know what to expect. If you cannot find someone to do this for you, solve questions on a white board or notebook so you get the feel of writing algorithms some place other than your code editor.
Practice asking questions to understand the scope and constraints of the problem you are solving. Once you are hired, you will not be a siloed data scientist. It is reasonable to bounce around ideas and see if you are on the right track. It is not always about getting the correct answer, which often does not exist, but about how you think through problems, and how you work with other people as well.
Practice the Skills that you Will be Tested On
Your preparation should be informed by the job description and the conversation with recruiters. Study the topics that you know will be on the interview. Look up questions for each area in books and online. Review your statistics, machine learning algorithms, and programming skills.
Additionally, Spring Board has compiled a list of 109 commonly asked Data Science Questions.
KDnuggets also has a list of 21 must know Data Science Interview Questions and Answers.
Follow Up with Thank You Emails
This is probably standard etiquette for any interview but remember to send a personalized thank you email within 24 hours of your interview. Also, if you have thought of the perfect answer to that question you couldn't solve during your interview, include it as well. Don’t forget to express your enthusiasm for the work that Company X does and your desire to work for them.
If you get an offer after your first round of data science interviews, Congratulations! Close this tab and grab a beer. If you are turned down, like most of us are, use the lessons you learned from your past interviews to prepare for your next interviews. Interviews are a good way to identify your areas of weakness, and consequently become a better candidate for future openings. It’s important to stay resilient, patient, and keep a learner’s mindset. Statistically, you probably won't get an offer for each position you apply for. Like the excellent data scientist you are, debug your interviewing process and up your future odds.
Other Great Data Science Blog Posts To Help Make You A Better Data Scientist!
How To Ensure Your Data Science Teams And Projects Succeed!
Why And How To Convince Your Executives To Invest in A Data Science Team?
Is your company looking to figure out who should become data scientists and how to start a team? You are not alone, even Amazon and Airbnb are starting internal universities to teach more of their teams the values of data science. Maybe your company needs help setting up some internal classes to help increase your data science an machine learning skill sets. Acheron provides multiple forms of internal education programs. They can be for managers, or analysts. One form is a quick guide to how to run a data science team! This a for managers and executives who are starting, or already have a data science team and want to ensure they are getting the best return on investment from their team and that their team members all feel challenged!
We took one sub section out and wanted to share a common question we get when we talk to executives. Who are data scientists, and who should become one! One such client told us they have loads of scientists, but wasn't sure how to turn them into data scientists, and who in their cohorts should really become one.
Below we will go over some of the top soft skills data scientists should have, and what type of personality should someone have before they enroll in some form of data science program. Whether this be an internal program, or external, like Galvanize, or a university data science certificate. In the end, data science is a skill that companies will need to harness to make sure they can keep up with the rest of their competitors who are already successfully implementing data science into their upper level strategy.
Who are Data Scientists?
Data scientist have to be driven individuals. They not only must be technically savvy, they also need to be proactively aware of their company’s nuances. If they happen to see a correlation or pattern, they will seek out how to access the data required and will bring possible projects up to their manager.
Being driven is great, especially when combined with curiosity. Data scientists love to ask why, and not stop until they find out the root cause. They are great at pinpointing that actual patterns in the noise. This is a necessary skill in order to peel apart the complexity and relationships various data sets may have. Occasionally, an individual may have a curious mind, but may lack the drive to act upon their inquiries.
Tolerance of Failure
Data science has a lot of similarities to the science field. In the sense that there might be 99 failed hypotheses that lead to 1 successful solution. Some data driven companies only expect their machine learning engineers and data scientists to create new algorithms, or correlations every year to year and a half. This depends on the size of the task and the type of implementation required (e.g. process implementation, technical, policy, etc). This means a data scientists must be willing to fail fast and often. Similar to using the agile methodology. They have to constantly test, retest, and prove that their algorithms are correct.
The term data storyteller has become correlated with data scientist. This skill-subset fits in the general skill of communication. Data scientists have access to multiple data sources from various departments. This gives them the responsibility and need to be able to clearly explain what they are discovering to executives and SMEs in multiple fields. This requires taking complex mathematical and technological concepts and creating clear and concise messages that executives can act upon. Not just hiding behind their jargon, but actually transcribing their complex ideas into business speak.
Creative and Abstract Thinking
Creativity and abstract thinking helps data scientists better hypothesize possible patterns and features they are seeing in their initial exploration phases. Combining logical thinking with minimal data points, data scientists can lead themselves to several possible solutions. However, this requires thinking outside of the box.
Data scientists have to be able to take large problems, like what ad to show to which customer, then based off of hundreds of variables effectively find the right solution. This means taking a larger problem and breaking it down to its smallest parts. Getting rid of noise, and variables that don’t help create a clear pattern. This can sometimes be a messy process. Being able to keep focused on the bigger problem is key.
Who Should Become a Data Scientist
The skills required to be a data scientist are constantly evolving and many companies are trying to find out how to train new data scientists. In the end, the real question is, who should become a data scientist?
Data science requires constant learning. Not just technology, but it also requires constant learning of new fields, specialties and situations. Especially as data science solutions further integrates into more and more departments of corporations. Becoming familiar with one set of vocabulary, and processes is not an option. Without having some bearing in each field limits the hypothesis and logical assumptions required to be made by a good data scientist.
If you are searching for a data scientist inside your company. They are probably already attempting to push into the field. With all the online material, classes, and meet-ups, an individual would have already taken steps to get more involved. If they merely talk about it, but never act upon it, they will act similarly on a new project or idea.
There is some requirement for computational or technical abilities. Excel is a great tool, but there is a need to be able to use more powerful and customizable tools. This includes programming, data visualization and data storage tools. There is no need to be a software engineer. However, data scientists have a general idea of how to make sure code is maintainable, robust and scalable.
Looking to start a data science team?
If you are looking to start a team of your own. Feel free to comment, or email us! We can do everything from point you in the right direction of readings if you want to do it yourself, to come and join you on your journey! Also, feel free to follow our blog. We will keep it up to date as we do new projects, and new questions about data science! If you email us a question, we will try to post about it!
We are a team of data scientists and network engineers who want to help your functional teams reach their full potential!