Effectively hire and retain a Data Science team in the midst of a crisis

The 2012 article ‘Data Scientist: The Sexiest Job of the 21st Century’ talks about the emergence of Data Science, with LinkedIn as a case study for how to get started.  Since then, many more companies have realized the value of Data Science, driving up the demand for talent.

The US was projected to need 2,720,000 data professionals by 2020, Until Q1 2020 Data Science roles showed year-on-year growth of 32%.

With the COVID-19 slowdown, companies need to do more with less and ensure that Data Scientists are engaged to predict the unforeseen while maintaining business continuity in face of possible recession. 

Engaged employees care about their work and contributing on behalf of the organization’s goals. Data Scientists tend to be curious, creative, mathy, high performance individuals and are high valued. Engagement is key to keeping them motivated and make them contribute.

Here are a few must do actions to drive engagement:

Hire Smart: 

Data Science is not a single competency – it’s a discipline with composite skills. Exercise parsimony with job descriptions and specify pertinent skill sets. Most data technologies are 5-10 years old and adding unnecessary details reduces effectiveness. It might be prudent to create job families in data with specific skill sets and aptitude to make hiring easy.

Reskilling existing employees to engage them as data scientists in the current slowdown can be an option. However, ramp up time, a lack of technical background, qualifications and aptitude in data are key screening criteria for such candidates and may limit the ability to upskill effectively.

Setting correct expectations to  a new hire is critically important. This can be a challenge as  ‘hired candidates end up working on the problems a company currently needs solved, rather than the “role” they may have been hired for.’ 

Custom hackathons to solve business problems can be useful to identify new talents. But hackathons need to measure  business adaptability, team dynamics and interpretability of winning solutions for a hire decision.

Build the Correct Culture: 

Data Science needs to fit in the overarching corporate vision. Data Scientists should know their work is impacting the top or the bottom line. Team members new to a business model need mentoring – to quickly understand successes and areas of opportunities. Data Scientists must be part of  business conversations, empowered to share ideas and motivated to be solve actual challenges.

Data Science is a team sport. Business stakeholders need analytical competency to ask the  right questions- ‘How much better are we than a coin flip?’A culture of leading with data is key. Then business stakeholders and Data Scientists are  partners in driving change to adapt in the current slowdown .

Hiring a few data scientists and letting them work in a silo is a recipe for trite insights, impertinent  predictions  and non-compliant recommendations.  Data Scientists need to be plugged into the business ecosystem across multiple touch points to be successful.

Ensure Engineering Support:

Whereas data is an important asset, poor quality and departure from standards result in inferior  data products,  reducing transformative effectiveness. Data Scientists need to be enabled with platform architects and product owners to specify instrumentation, standards, life cycle and test of ingested data. 

DevOps time and effort is crucial for success in order to implement or maintain models, algorithms and ETL/ELT. In case DevOps is constrained, enabling Data Scientists with DataOps competency is key.

Establish the Right Organizational Design: 

Data Science organizations need to be owned and sponsored by a CXO with organic alignment. Many shared services lack ownership  and their work remains unrecognized and unintegrated. 

A Data Scientist should be assigned measurable and attainable goals, encompassing technical capability, customer success, business objectives and innovation. In the near term, Data Science goals ought to be tied with Revenue Retention, Loss Prevention and business continuity.

Encourage diversity. Employers now see the opportunity to have a large part of their workforce working from home. This opens up new opportunities for quantitatively smart but untapped demographics- stay at home workers. 

Also, a seasoned Data Science leader is key to a scalable, agile Data Science organization who can calibrate, mentor, reduce waste, sign off ROIs  and increase business effectiveness of the team.

In conclusion…

Data Science is  an established cornerstone to corporate success in the age of digital transformation. Leadership should look at Data Scientists as strategic assets and plan for a sustainable organization. The first step in doing so, is to provide best in class engagement.