The traditional data science team has undergone a period of drastic change in the past two years due to COVID-19 and the “Great Reshuffle” that has followed. This period has witnessed relocation of staff to remote locations, changing business models in response to the pandemic, and challenges in attracting and retaining talent. At a time when reliance on data science is greater than ever, these challenges have led to a need to innovate roles and responsibilities within data science teams across all industries.
We’ve heard from our clients who previously had their pick of local talent that they now find themselves competing with companies across the country.
The geographical dispersion of talent combined with a smaller or newer team due to staff turnover has, in some organizations, caused project and communication inefficiencies. The new remote work option has resulted in advantages and disadvantages for employers looking to hire, as it opens up a wider labor pool but increases competition. We’ve heard from our clients who previously had their pick of local talent that they now find themselves competing with companies across the country. Resignation of employees can cause huge setbacks to an organization when team members who have deep understanding of code or data science processes leave. Professionals that have moved to the work-from-home model have had to learn how to work and communicate differently, and such fractured communications have exacerbated any inefficiencies that may have pre-dated the pandemic.
Due to these workforce challenges, the struggle to keep up with data science demands requires innovative approaches to staffing and reporting structures. Businesses that employ data science strategies have been attempting to reduce the challenges by investing in tools to standardize processes and more closely integrating data science and technology teams. While these attempts to solve data science gaps may help streamline processes, they don’t solve for the shortage of data scientists.
By augmenting internal groups, our teams not only can provide “surge” capacity to help with project backlog, but also can be a cost effective way to bring to bear the many different skill sets required on a typical data science project.
One way to adapt to and overcome such challenges is to supplement or expand existing resource pools with a flexible supplemental third party data science team. Fulcrum Analytics offers a risk-removing solution for organizations who need to fill short-term or long-term resource gaps with experienced talent. We offer a Data Science Acceleration Team made up of the right mix of US-based data engineers, data scientists, software engineers, and business analysts to supplement each company’s unique data science program. Not only can having a per-project or consulting based team by your side aid in smoothing out changes to internal teams and add value to overall business structures, but we help streamline the process of finding and executing the highest impact projects with our skilled experts.
By augmenting internal groups, our teams not only can provide “surge” capacity to help with project backlog, but also can be a cost effective way to bring to bear the many different skill sets required on a typical data science project. When deployed on a retainer basis, a commitment for a set number of full-time resources can access the deep expertise of many more consultants, something difficult to achieve with an equivalent number of internal hires. In addition, having an external consulting firm be jointly responsible for providing continuity and knowledge management can be a real asset during a time of high staff turnover.
Don’t let mounting staffing obstacles set your data science organization back. From project work to product development, we thrive on our ability to jump into new projects and quickly produce results. Click here to learn more about how our flexible data science capabilities can optimize your team, strategy, and profitability in the years to come.