What Makes a Good Data Scientist? Fundamental Things to Look for in a Data Scientist: In Closing:

Data science as a practice has exploded in the last 15 years, and is expected to continue its rapid growth in the coming years. This growth is supported by an ever-growing number of academic programs, training curriculum or mid-career options available to enter the field.

And, despite the economic uncertainty of the moment, companies remain focused on data as a foundational aspect of their business strategies, reinforcing the level of demand for Data Science as a necessary part of their operations.

This explosion has been mirrored by an evolving landscape of tools and technologies, including new languages, statistical software, and data wrangling tools.. Data Scientists themselves drive much of this evolution, as they look for new and more efficient ways to perform their work.

Industry experts and solution architects also contribute to this new landscape as they develop more powerful solutions, and many of today’s data-mining, statistical or visualization tools represent dramatic improvements from the early years of data science.

But, while this increased availability of tools has made the lives of data scientists easier in many ways, it can make it harder for organizations to know how to properly evaluate incoming data-focused talent, or up-skill existing employees in the right way.

Here are some tips to consider as you look to find or grow your organization’s data science talent and capability set.

Fundamental Things to Look for in a Data Scientist:

Despite the ongoing evolution of data science, there remain a few foundational elements that each data scientist – no matter how tenured – should have.

A solid understanding of statistics and mathematical concepts: Data science is founded in mathematical principals. To interpret results, any data scientist must be able to decipher the results of a regression model, cluster analysis, or other comparison sets of data. Look for talent with a demonstrated academic or professional background that involves this skill set.
A flexible technological skill set: Just as comfort with mathematical concepts is foundational, so is a flexible capability with technology. Having a strong grasp of programming (Python, in particular), statistical tools (such as R Studio), or visualization tools (such as Tableau or ggplot), are critical aspects of a data science skill set.
Solid SQL and other data-wrangling skills: It’s a well-documented fact that a data scientist job often involves a large proportion of data cleansing and organizing. To support this fundamental aspect, data scientists must have a good to great knowledge of SQL or a comfort with using data wrangling tools (such as Alteryx or Trifacta) to access the information needed to perform their work.
Curiosity and willingness to fail fast: There are rarely clearly defined problem statements or data sets for data scientists working in industry. Therefore, a data scientist must be comfortable asking questions, probing the problem, developing an approach and pivoting quickly if the findings don’t address the objective.

In Closing:

While Data Science continues to evolve, many of the core things needed to be successful in the field remain constant. Getting started and finding the right talent can be a challenge, but focusing on these foundational aspects of your talent decisions will help you to better prepare for incoming talent, or to identify existing employees best positioned for success in your data science team.

Need help getting started? We’re here to help. With skill assessments, recruiting and staffing services focused data science and analytics, we can help you find the talent you need to make progress in your analytics journey. Contact us today.