By Wojciech Gryc on October 20, 2020
An important part of seeking a data-oriented job is putting together a data science portfolio. Portfolios help candidates stand out to hiring managers and potential companies they could work at.
Most data professionals are subject matter experts and have deep areas of expertise, but this also makes it difficult for people to understand what they actually do. A hiring manager or recruiter reviewing 100s of resumes and CVs doesn't have the time to understand each candidate's expertise, jargon, or nuanced innovations.
Help the hiring manager understand your capabilities by emphasizing the end-to-end projects you've organized and launched; make sure to describe everything in plain language.
First, begin by picking one project that best emphasizes your fit for a specific role. Describe the problem you addressed and how you solved it. Limit your discussion to 3 or 4 sentences, and use visual cues (screenshots, diagrams, or photographs) where you can. Most importantly, avoid jargon!
Your ultimate goal is to ensure those reading your project overview will be able to describe it to someone else. It's OK if they miss the technical nuances, as long as they understand what problem you solved and how effective your solution was.
Here are some specific pieces of advice:
The last point is critical: given the many candidates recruiters and hiring managers speak with, being able to summarize the end result without any jargon will help those hiring for the role remember it, speak to their colleagues about it, and ultimately evangelize you as a candidate. If you have a visual component to the portfolio, then that makes it shareable, too!
Candidates often ask if having a personal website is helpful, and our recommendation is yes. You need not have a fancy one, but the point is to have your latest resume there, along with links to your portfolio. Leading with a visual, pithy (3 sentences!) summary of each portfolio item will enable the hiring manager to review the work quickly and share it easily with colleagues. Most importantly, it'll help them understand how you will contribute to their own company's data science objectives.