Data Science & Analytics - Planting The Seeds To Build Talent

An ardent professional holding vast experience across range of verticals, Ashish has a strong track record of partnering with top-level decision-makers in major corporations to drive results.

In the last few years, there has been a steady increase in the buzz and coverage of how every organization is increasing investment in data science and analytics capabilities, and its critical significance to competitive advantage across industries. There is a constant stream of stories on from the employers’ perspective related to data scientist hiring how it is difficult for organizations to fill positions, the shortage of talent, the high compensation costs and others. Moreover, there is considerable angst among hiring managers and HR executives over the high rates of attrition in their data science teams.

In my view, from the employer side, these trends point to a considerable extent towards lack of a holistic approach to finding and retaining analytics talent an approach that takes both short term and long term requirements into account, from both employer and employee perspective. Such an approach should find talent that fits you organization and then develop and grow them once they join so that they continue to stay and keep contributing at higher level over time. Similarly, from the employee/candidate side, the aspiring data scientists also have lack of clarity and awareness on what it takes to succeed long-term in a data science career, and they may not be focusing on the right mix of skills to grow. From my long years of experience managing large teams in this talent market, I strongly believe that currently there are gaps in hiring and employee talent development approach that are keeping both employers and employees short of a mutually satisfying outcome.

The Secret Sauce of Data Science Talent
Organizations come in all flavors due to differences in their cultures or nature of work. As such, a one size fits all approach to hiring data scientists rarely works universally. At the core, Analysts and Data scientists need a combination of four kinds of skills to be effective over the course of their careers Statistics/Maths, Technology/Computers, Communication/Story telling and Business Acumen. Statistical/Math skills help make sense of the large quantities of data and to draw inference. Technology skills firstly help the data scientist extract the right data from the vast set of internal or external sources, and secondly develop tools to automate and run processes that can convert data into information/insights. Business skills help the analyst understand the business questions from internal stakeholders or clients, and also to ask the right questions from the data, both of which together help tie the answers back to those questions. Story-telling skills help make the insights and recommendations from their analysis/models easy for rest of organization to understand and implement into decisions or actions. The secret sauce is in getting the right mix of skills in the person and team to get business impact from this type of talent.

Each organization may expect or require a different mix of these four skills from a candidate from Day One of joining. For example, in small/startup organizations which require each employee to put on multiple hats and be agile from Day One, a more balanced mix of all four competencies makes for the most productive employee. On other hand, in large organizations with highly specialized roles and functions, there may be a need to hire people with particular strength in some areas (say Stats/Math or Tech/CS) on Day One and provide them support on the other areas from other teams.

I can say that such professionals also come in different strips and shades in terms of these four areas. While being good in one or two areas may be sufficient in doing well in the initial period after hiring, all hires who are able to keep growing and adding value longer-term to the organization typically grow skill in the other areas. Therefore, it makes sense to hire with clear evidence or potential of proficiency at least two of the four areas to ensure good probability of short-term and long-term success. The remaining (missing or deficient) areas are important to grow and develop after the candidate has joined and the goal should be reach at least a working level of proficiency in those areas so that they do not cause limitations in the employee’s ability to contribute to the organization as their career evolves over time. In other words, the manager or HR needs to continue efforts on those areas longer term to make the employee more well-rounded after hiring and onboarding.

Applying the Secret Sauce to Hire & Retain the Best
This brings me to a few important ingredients of the recipe required to hire the talent with best potential for long term fit for Data scientist roles in an organization. To start with, currently there is overwhelming focus on Math/Stats/Tech skills during hiring and not enough attention or weight age is paid to assess candidate’s Business acumen skills and Story telling skills. The resume screening and interview process should ensure that these are also evaluated. For example, some candidates with moderate tech/math skills but stronger business/story telling skills can succeed at the role but may be overlooked by hiring managers who are focused too much on the former and ignore the latter. Emphasis on only one or two dimensions could result in hiring candidates who may be successful initially but may struggle to grow further in the organization.

As part of this process, it is critical to nudge and push the employee outside comfort zones and to make them aware that they need to add more dimensions to their profile

Holistic approach to recruiting and hiring is the first step. Once the data scientist has joined the organization, there are more proactive steps needed as they proceed on their careers. Development plans needs to be discussed and finalized by the manager and HR in consultation with the employee to help provide training and work experiences that make the new hire more well rounded in their skills over time. As part of this process, it is critical to nudge and push the employee outside comfort zones and to make them aware that they need to add more dimensions to their profile. From my experience over the year managing and mentoring analytics professionals, there often are blind spots in the employees that create difficulties for them as they progress in their careers e.g. those employees who have very strong stats/math/tech skills may ignore building the business/story telling skills and vice versa. In both cases, their careers can be negatively impacted and potential unfulfilled due to lack of holistic development.

Conclusion Long term benefits from a holistic approach
So my advice to HR teams, recruiters and hiring managers is that they should make sure that the hiring process casts the net wide both in terms of evaluating the four skill areas mentioned above as well as in terms of the type of candidates who may have those skills. This will ensure that we give an opportunity to maximum number of candidates with chance to succeed in a Data Scientist career to be considered for the open roles. Once hired, the new employee needs to be given opportunities and made accountable to learn, experience and build mastery not just on the strong areas that got them hired, but also the other areas among the four described here. It is important that we avoid blind spots that may be there in managers or employees regarding what skills are required to succeed as a data scientist. The holistic approach describes above requires additional effort from both the organization and employee, but pays rich dividends to create a win-win solution that grows human capital and long term business impact from investments in data science capability.