Pathrise CEO and co-founder, Kevin Wu says, “Data science will continue to expand as the tech boom does, so now is the time to jump in.” As more companies realize the importance of strong, clean data, the need for analytics- and statistics-minded people grows, too. So, what does it take to find a great job in this highly lucrative field? Education, experience, and the ability to showcase your impact.
Candidates with the right background to get into data science and analytics positions might still have trouble finding a job, even with the high demand and low supply. This is usually because of the way they search, apply, and interview for the roles. Candidates who cannot properly highlight their successes in previous positions or in coursework will not be successful in their job search.
To start, prospective data scientists need to have a strong resume that focuses on the impact they have had in previous work experience or in the projects they have done. The best way to do this is by including quantification and context in all of the resume statements. For example, a weak resume statement for a data scientist might look something like this, “Analyzed data and created a presentation with results.”
While this is probably an accurate statement about what the candidate did, it does not provide any context of why the work was important nor does it show how much data was analyzed, how many people saw the presentation, and what came out of the work. Candidates who do not explain the scale and results of the work that they did have trouble showcasing why their work was impressive and how their work will be impactful for the company that they are interested in.
Instead, that weak resume statement can be strengthened like this: “Analyzed 4,000 rows of marketing data collected over 4 months and created a 30 page slide deck to present findings to executives and shareholders, resulting in an altered marketing plan that increased conversions by 40%.” While this sentence is much longer, it creates a clear picture in the reader’s head, quantifies the scale of the work as well as the results, and clearly showcases the impact of the work. With this additional information, recruiters and hiring managers are much more likely to stay engaged and know from the get-go that this candidate can be successful in the role.
After candidates optimize their resume to add quantification and context, they need to show they will be successful at the company through their interviews, as well. Just like on their resume, data scientists need to be specific, thoughtful, and numbers-heavy in their interview responses. Utilizing the STAR method – Situation, Target, Action, Result – when thinking through answers to questions is a good way to do this. Similarly, candidates should keep their responses somewhat brief and then offer to go into more detail if the interviewer would like. This helps avoid rambling and keeps the ball in the interviewer’s court.
These tips should help candidates looking to get great jobs in data science and analytics, one of the fast growing fields in the tech industry right now.
