Data Science: Tips for finding a new role

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The need for Data Science candidates is growing and this trend isn’t going to change any time soon. In the US, this need is expected to create 11.5 million jobs by 2026.

Despite the fact there is a surplus in demand and not enough talent available, the interview process is still a major hurdle for even the most experienced of candidates.

We’ve put together some simple tips that will help you land that dream Data Science job.

The Application Process

Take the time to read the job description – This may seem intuitive when applying for a new role, but it’s easy to skim through a job spec and miss key details.

Tailor your Resume accordingly – As a Data Science candidate, you have likely worked on a variety of interesting projects and have a wealth of technical experience that is hard to condense into one or two pages. Pick out the key skills/responsibilities identified in the job spec and focus on these. A four-page resume is too long and will no doubt contain a lot of information that, while great, will be irrelevant to a particular position.

Demonstrate Why You Did Something– Examples of your responsibilities when working on a project are great, but the Hiring Manager wants to see an awareness of Why these were important. What was the overall business objective of you developing scalable Data Pipelines and how did it relate to wider business goals?

Include outcomes –The Hiring Manager will also be looking to see how these projects impacted the business on completion. Notice the difference between “Working on the development and integration of a Machine Learning model for multi-label classification” and “Working on the development and integration of a Machine Learning model for multi-label classification that increased Data Accuracy by 12%”. This shows the value your work can add from a broader business context.

The Interview Process

Data Science is an incredible broad field that encompasses far too many technologies to mention. However, there are some common trends and tips that are useful regardless of the role.

Research the company – A common pitfall is brushing up solely on your resume and what you can bring to the table, but it is equally important to know your potential future employer. The Harvard Business Review called Data Science “the sexiest career of the 21st Century” for a reason. Many of these companies are developing life-changing applications and the Hiring Managers are going to be extremely passionate about that. If you can understand the problems a company is trying to solve and exhibit a similar passion, this will go a long way.

Know Who You’re Speaking to – Researching the Interview panel on LinkedIn and on the company’s internal site can be a great way to gain insights into who you will be speaking with. Do you have any common connections you can leverage? Have you come from a similar background that will make for a good talking point?

The Technical Test – You would be hard-pressed to find a Data Science role that doesn’t include some sort of technical test, and a resume only tells half the story. It may say you have excellent Python experience but an employer is going to test this. There are a variety of free resources available to brush up on technical questions, such as HackerRank. Some employers will lift questions directly from online resources like this.

The Competency Question – A key area recruiters will recommend preparing for. This is one of the main pitfalls in an interview, particularly for those who are more analytical in nature - not due to lack of experience but poor preparation. Competency questions require candidates to provide real-life examples as the basis for their answers. A common example might be: “Tell me about a time when you had to overcome a dilemma”.

These questions are particularly important in Data Science, when it’s a case of WHEN not IF a problem will arise. Employers want to know how you will react. Think back on your career to an incident where something went wrong: What was your initial reaction? What did you do to rectify the problem? What did you learn from that situation?

There are a variety of online resources available highlighting the most common questions Data Scientists are asked from companies like SAS.

A great technique for planning your answers is the STAR method. This helps you to create an easy-to-follow story with a conflict and a clear resolution. You should aim for an example that highlights the extent of your experience, giving enough detail without rambling.

  • Situation – Set the scene by describing the context. What was the issue you/the business was facing and what was the Objective? Be as specific as possible here to set the scene.
  • Task – What was your specific responsibility in relation to completing the relevant task?
  • Action – What specific actions did you personally take to overcome this challenge? Instead of referring to the team here, focus on what you did as an individual.
  • Result – What was the outcome of your efforts? If you can, quantify this success with figures: “As a result of my actions the team was successfully able to increase the accuracy of data by 12%.

Ask Questions – The interview is not just a forum for the employer to test your suitability, it’s also a chance for you to dig deeper into the company and the role. This shows the employer you’ve taken an interest in the company and are looking at it as a long-term investment. How do they plan to scale their technology? Are they in the process of developing any new applications/entering new markets? What does career progression look like for the typical Data Scientist?

While the responsibilities and requirements vastly differ between Data Science roles, many of the tips discussed here will be prevalent regardless of job title. Taking the time to brush up on your technical skills and having a great understanding of the challenges your prospective employer is facing may be the difference between landing that dream job and having to start your search from scratch.

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