Businesses across industries recognize the value of data science, but a scarcity of data scientists prevents them from implementing it to their advantage. With so many organizations competing for data science talent, an inclusive data science strategy is not just good for business and ethics but also a requirement.
Companies that use data science to streamline processes and power new applications reap various benefits, including operational efficiency, increased revenue, and innovation. With so many businesses fighting for data science talent, an inclusive data science strategy isn’t only good for business — it’s also more ethical. Data scientists, not data, are the strategic resource of the twenty-first century.
However, to scale data science teams and reap these benefits, companies must rethink how they hire and support data scientists.
People, Process, and Technology
Every company that wants to scale its data science skills must embrace this diversity of data scientists since there are just not enough of them with a particular profile to hire exclusively. Most organizations, however, consistently operate against their own interests. They try to over-standardize data science career trajectories and job descriptions, restricting data scientists to a limited set of tools. All of these activities have a negative impact on an enterprise’s ability to scale.
Instead, businesses must plan for and create a workplace that welcomes all data scientists. They must address the people, process, and technological components of data science diversity.
Recruit Professionals with Diverse Profiles
Hiring managers and recruiters must actively seek applicants from different academic backgrounds, skills, and experiences. They should create various profiles, ideally linked to multiple projects, rather than a one-size-fits-all profile. Individuals should be hired based on their ability to learn and apply necessary strategies and their transferrable skills, as evidenced by their previous achievements. Above all, they must resist the urge to screen candidates using keywords associated with specific degrees, programming languages, tools, and frameworks.
Diverse Teams Should Be Encouraged
Companies must nurture their diverse teams once they have hired them. It’s easy to design a skewed data science career path with a predetermined set of skills at each rung. As a result, data scientists are dissatisfied, productivity is low, and turnover is high. Instead, creating unique career paths with a high degree of flexibility is critical. Companies can foster a sense of belonging by making formal and informal avenues for collaboration within and between data science teams.
Empower Professionals with a Diverse Toolkit
Every data scientist comes equipped with tools that they have spent years honing their skills. And while every smart data scientist wishes to learn new ones, there is no faster way to frustrate a data scientist and diminish their productivity than to block them from utilizing the tools they are familiar with and most suited to the project. To boost productivity and collaboration, companies don’t need to standardize tools.
The best data science platforms now allow them to leverage proprietary and open-source data science IDEs, packages, languages, hardware and distributed compute frameworks. Furthermore, regardless of the tools used to produce them, these platforms allow data scientists to collaborate and exchange results, model artifacts, code, and so on. In addition, they provide infrastructure security, control, and shared access.