Resources to Help You Build the Ultimate Data Science Team
Data scientists are among the most sought after professionals on the planet right now. In fact, the role of data scientist has been called the sexiest job of the twenty-first century. Given the combination of programming and mathematical skills, as well as business acumen that data scientists can bring to their work, that’s not surprising. Practically speaking, however, that means that finding the right talent to build a successful data science team can often be incredibly difficult.
In this resource guide, we’ve compiled a collection of our best articles and podcasts designed to help you build a successful data science team. You’ll find practical tips about what to look for in data scientists, what roles to hire to build a successful team, a template for a job description and more.
We’ll continue to add more content to this page over time, so check back periodically for updates.
Is training more people in data science the way to solve the skills shortage and build more data science teams? Or do we need to look to software-based solutions that can help bridge the gap between organizations’ data science needs and their ability to attract and retain data science talent? In this post, we look as the democratization of data science and its impact on building effective data science teams and whether you need more data science talent, better tools, or a combination of both. Read more.
Looking for a data scientist who is an expert mathematician, a fantastic programmer, a deep thinker on business issues and an outstanding communicator? Individuals with all of those skills are hard to find because they’re unicorns. In other words, they don’t exist. And, even if they did, they’re just one person and wouldn’t be able to scale the way that a team can. In this post, we discuss why data science is a team sport, and outline the various roles that you should consider hiring to build your own effective data science team. Read more.
Data science is a field at the intersection of mathematics, technology and business. It draws from many skill sets including statistics and programming, as well as from deep domain expertise. But that doesn’t mean that individual data scientists have to be multidisciplinary. In fact, searching for a polymath data scientist who can do it all is likely to lead you to failure. That’s because even if you find a person who has it all, you’re unlikely to get the depth of skill in any one area (math, technology, business) or the scale that your organization needs. In this post, we explain why you should focus on the math. Read more.
Looking for a job description for a data scientist but don’t know where to start? In this post we provide a template of a job description that we’ve used with Georgian Partners’ portfolio companies to help them hire key roles within their data science teams. This simple template is very effective at generating lots of interest in whatever data science roles you may be trying to fill on your team. Read more.
When the Georgian Partners Impact Team hosted a discussion with our portfolio company CTOs on the topic of building data science teams, a number of practical tips emerged. In this post, Ben Wilde compiles the nine best tips that came out of that discussion. Read more.
Want to get some great advice on the go about how to build successful data science teams? In this episode of the Impact Podcast, we discuss all of the strategies, pitfalls, and other considerations to keep in mind when building yours.