A Candidate’s Guide to Data Science

A candidate's guide to data science

Entering the Data Science workforce can require fielding, navigating and patience. Even with the technical chops and an academic foundation, getting into Data Science is not as easy as some perceive.  

Below outlines advice for novice Data Scientists looking to enter the expansive Data Science industry.

The CV

This one to two-page document needs to cover a lot for Data Scientists. Beyond previous experience which most entry-level candidates lack, the CV focus should be on at least one project whether that be a Data Science, Data Analytics or Machine Learning project, a published scientific article or even a coding tutorial. On your CV, employers are looking for practical applications of your skills so avoid recounting your technical skillset and rather highlight any practical projects that have resulted in measurable business solutions. This is where you should infuse your skills and technical knowledge in relation to these projects. Do not forget to link soft skills into the CV. Communication and interpersonal skills are a huge part of a data scientist’s job and should be championed on the CV.

Finally, the ‘Skills and Certification’ section should not be negated for those without workforce experience. With keyword searching a common filtering tool used by recruiters, this is the section to highlight those key Data Science terms like Python and other coding languages, and Machine Learning. Although avoid getting carried away here, recruiters will see straight through a Data Scientist with a skills list that overpromises.

The Project Profile

Whilst a CV is vital, the project profile is the best way of highlighting your value to employers. It is the most effective way of showing employers your skills and value, rather than just telling them. Kaggle, a crowd-sourced platform described as Airbnb for Data Scientists, is a great platform for novice Data Scientist needing to build their project profile. However, Reshama Shaikh notes in her article To Kaggle Or Not, Kaggle’s are “a complement to your other projects, not the sole litmus test of one’s data science skillset.” Aside from these Kaggle competitions, employers want to see projects that candidates are passionate about so pick a topic of genuine interest.

The Job Hunt

For those light on the industry experience, considering an internship may be perfect as they provide both industry experience and can often lead to a permeant job offer. They are worth consideration for those needing relevant experience quickly or wanting to expand their professional network.

Ensure you have an online presence across LinkedIn, Twitter, blogs and beyond is vital for Data Scientists and even if not actively searching for a job, attending meetups, events and conferences are promising networking platforms. Meetup.com and Eventbrite are two good sources to find relevant events. Employers unanimously prioritize relationship-based applications over those ‘cold’ applications so even if not actively searching, developing industry relationships is vital for novice Data Scientists.

The Interview

Whilst each company has a unique approach to their Data Science job interviews, Dataquest has identified three questions below as the main considerations driving employer’s decision making;

  1. How interested are you in the role?
  2. How well does your skill set match the job’s requirements?
  3. Would you be a good ‘culture’ fit?

In the interview process, which can take up to three weeks, they typically begin with a screening phone call intended to quickly eliminate any under-qualified candidates. Assessment of your technical knowledge and coding-related-skills will unsurprisingly occur, along with at least one interview examining your soft skills and assess if you would make a cultural fit with the company. The employer's questions surrounding technical can often be extracted from the job description. If the requirements specify knowledge of Python or R coding language, for example, it is almost certain the interviewer will ask questions surrounding this.

If interviewing for an entry-level position, be prepared to answer a lot of questions about any previous projects. Interviewees should be familiar with their projects inside-out and be armed with answers to specifically what you did, how you did it and any statistics and programming concepts used in the projects.

Do not forget to arrive prepared with questions to ask. Employers and recruiters agree that the questions candidates ask are far more influential than the answers they give to questions. These questions leave a lasting impression and are a valuable chance for candidates to learn more about the company.

Finally, be prepared for the process to take time. For Kelly Peng it took 475 applications and 50 phone interviews over a six-month period for her to land a Data Scientist position at Airbnb.

The path into Data Science is clearly not linear but with a comprehensive CV, impressive project profile and refined interview skills, budding Data Scientists are best equipped to land the perfect position.


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