Almost three years ago, I switched from a career in academia to a career in business in a data science role. This used to be somewhat of a rare event, but today it is commonplace: not only is there a shortage of data scientists, but also people change careers faster than ever before. I’d like to share some thoughts on the data science skills that I found most helpful for this transition.
[Related article: Companies Hiring Data Scientists: Summer 2020]
1. Willingness to learn. Learning does not stop when you finish school, and ideally, it should never stop! The journey is easier when one is curious by nature, but either way, it’s good to make room in your schedule and deliberately plan the next items to learn.
As an example, if I look at the programming languages I use, it was Matlab at first and then I switched to R. Learning R happened the old style of buying the book, writing code snippets, and making lots of mistakes. By the way: if your code works fine the first time you run it, that doesn’t mean the results are right – and more often than not they are not! This year, I am learning Python, and it is very likely this is not the last language I learn before I retire.
2. Common Sense. As I have just explained above, most of the time the job is not only about obtaining a result, but also about knowing if the result itself makes sense. These days, the meaning behind the results is obscured by the increasing sophistication of tools – which can become veritable black boxes. AI, especially, has become very easy to use. With one line of code, you can download a package and get access to tens of functions that will cluster, analyze the sentiment of your text, or model your data for you. It’s a good practice to ask yourself “what results would I expect?” before actually running the code. If the actual results do not agree with your expectation, understand why; this is how most insights are obtained.
When common sense is switched off, there is room for algorithms to become “evil.” The ethics of AI algorithms is a field in itself – some good references to start learning about it are Cathy O’Neill’s Weapons of Math Destruction or the Future of Life Institute website. More recently, predicting crime using face recognition generated strong reactions from the AI research community.
3. Storytelling. This data science skill is about explaining complex things in an easy-to-understand language. Storytelling is creating value transversally in the company. Can you explain your results to people outside of your team, say to marketing or sales folks? This is of paramount importance as they will be the ones explaining the benefits of your work to clients. To achieve this, you will need to mix the right narrative, with effective analogies and compelling visuals. And if you cannot explain your work in clear and unambiguous terms, then ultimately, clients will not understand what is valuable about your product.
In my “previous life,” this was not a skill I had to perfect. Most of the time, I was surrounded by experts in my field and I virtually never needed to explain my work to someone who had little or no prior knowledge about my subject. Today, this skill is critical, and I am often involved in meetings where I get to explain how the data and analysis behind our product combine to solve a business need.
4. Team spirit. As business roles are becoming more specialized, most value is inevitably produced within cross-functional teams. In these conditions, teamwork has become crucial, and nowadays a team is as good as the level of cross-sharing amongst its members.
It is not that I was not used to working with many collaborators previously – that I was. What changed now is the profile of these collaborators. On a given week I may provide insights to the marketing team for a communication campaign, help the sales team with preparing a client meeting, work with the development team on certain product features or provide feedback on the UX design of our products. Interacting with so many different stakeholders takes some practice, but it is sure to benefit the entire team!
5. Initiative. This last data science skill is about making the best out of your job. Make your job yours! Crafting, reimagining, and ultimately growing your job is a long-term exercise, one most of us have to do at one point or another. The World Economic Forum puts “initiative” on the third position in their 2022 trending skills list, and this makes sense in the face of the large wave of changes transforming the workplace, whatever the industry.
The job I stepped into three years ago is very different from the job I do today, and in three years it will likely be more different still. Knowing which dimensions to add (or subtract!) to my job requires a lot of thinking about the different things I am doing today (what I am doing, why and how). It also requires looking at the things I should be doing – given the larger aim of the company and also my own interests and strengths. The best way to go about this is to make small changes frequently, rather than trying to change the entire job from one day to the next!
And that’s it for my data science skills list! Do you agree, disagree?
Happy to hear your own thoughts and transition experience!
Editor’s note: Be sure to check out Gabrielle’s talk at ODSC Europe, “Your Future, Today. Using NLP to Advance Your Career” this September 17-19! This talk will raise awareness on the importance of skills (hard and soft) in career progression. The audience will learn how career paths can be built using a skill-based approach.
Gabrielle Fournet is the Head of Data Science at Boost.rs, a startup focusing on people’s professional development. In this role, Gabrielle is responsible for building and maintaining a world-class database of jobs and skills across 27 major industries. In addition, she develops algorithms to recommend meaningful career paths to the Boostrs users and help them progress in their careers.