How to build a data science dream team
There's little doubt that getting to grips with data can drive powerful results for businesses of all sizes – whether that means identifying process efficiencies, forecasting future requirements or better calculating demand.
Given this, it's increasingly important for businesses to have highly capable data science talent on hand – and there are several ways that organisations can go about developing this resource.
Developing new areas of expertise
The first is to simply upskill existing IT staff members, calling on them to handle the organisation's data and to manage and maintain modelling processes. MLaaS solutions can be useful here, enabling teams to make fast progress when it comes to tackling datasets. This approach does have limitations, however. While it's relatively fast to set up, for example, it may ultimately be expensive; and it may well mean that processes are set in motion without expert supervision.
A more nuanced approach sees data science experts working in liaison with the broader digital team. This means that infrastructure responsibilities can be delegated to other staff, while the specialists handle data-specific tasks. This enables a flexible, scalable approach, while bringing expertise in-house. It can also provide the kernel of a full data science department. While this last option may be relatively expensive, it offers the most potential, enabling data scientists and engineers to tackle complex problems across the organisation.
At the planning stages, there's also the question of just what is meant by data science. Before hiring data scientists, engineers need to be in place to oversee how data is being collected and stored. In many cases the roles and responsibilities of data scientists and data engineers are conflated – so it will be important to define exactly what is wanted from a data department.
Carla Gentry, Data Scientist at Analytical Solution, argues that the single most important factor for an effective data science team is to have a wide range of skills on tap. “Ensuring that all team members play to the own individual strengths is vital,” she says. “I'm not the fastest programmer in the world – my true strength is in interpreting the data – so if time is a factor, having a team member to write the code would be more efficient. And depending on clients, we may need to have specialists in Oracle, Microsoft, MatLab, Python, SAS, Apache, and so on, so having a well-rounded team with experience in more than one coding language, and more than one platform, is a must.”
The very first hires for a data science team will likely be subject experts with a proven track record who can act as pathfinders for the organisation. Beyond this, generalists – appropriate to the organisation's level of development – can add manpower, while keeping the team flexible. Of course, when fleshing things further out, there's room to be open minded and to look for people who display aptitude while having a shorter CV. After all, an ability to learn and adapt will always be invaluable – and recruiters shouldn't just look for a shopping list of skills.
“Don't let buzzwords kill your team,” recommends Carla. “I've failed a hundred pre-employment tests [due to] test anxieties, but I've been very successful in my 21-year career as a data scientist. Having a good recruiting team that actually knows what a data scientist is... that's the big hurdle.”
The attributes that count
When it comes to hiring, Carla also points to the importance of personality, of specialising within the team and of being able to communicate. “[The next factor to watch out for] is ego and the ability to work in a team environment,” she notes. “Then, of course, you'll have to sort out which talent is best at what, who can program versus who can interpret. Lastly, someone has to wrap this all up and put a nice bow on top, so someone will have to have the ability to present your findings in a method that is understandable [and] without statistical terms that the client [or other stakeholders] won't understand.”
Of course, the precise skill-set that is needed will depend on the requirements of the team and the specific role being recruited for – with responsibilities spanning analytics, algorithms, big data processing, coding, databases and frameworks. A Chief Data Officer will need deep-level expertise in the field, as well as programming skills and an ability to lead. Data scientists will likely have experience across R, SAS, Python, Matlab, SQL, noSQL, Hadoop and Spark; while data engineers will also benefit from a background in Java, Perl, Python, Ruby, and C++. Machine learning engineers, meanwhile, will likely need some experience of Java, Julia and Scala.
Given the penalties that can be levied for data breaches under GDPR legislation (with Marriott and British Airways recently receiving fines of £99m and £183m respectively), security and effective data management are also increasingly important considerations for the data team
In the big picture, investing in a data science team will almost inevitably add another string to an organisation's bow when it comes to digital transformation. What's more, it may very well pay dividends in cutting costs and helping the organisation to identify and make the most of new opportunities – meaning that it's a process that is very much worth committing to.
“[Having a specialised data sciences team means] you'll have the talent to ramp up quickly on projects and hit the ground running,” comments Carla. Having committed data specialists is a crucial stepping stone to being able to tackle data challenges – and these individuals can play a key part in the DevOps team. What's more, at the larger level, the department can become a key competitive differentiator for the organization, driving change and progress.
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