data scientist 2.0

I’ve had the pleasure of working with Michael Young and his team at MBN Solutions over the last decade. As I mentioned in a previous post on recruitment, I’ve found them to stand apart as a recruitment agency that actually understands this space.

So, I was delighted to be offered this guest blog post, by Michael, on the work they are supporting (with The Data Lab) to understand and place Data Scientist 2.0s.

What does he mean by that new buzzword? Over to Michael to explain (in the first of two posts sharing their findings)…

The need for Data Scientist 2.0

Increasingly fast-moving, transformational businesses require key staff with data science and analytics skills. Today, we are seeing this spread even more to traditional businesses. As we see new approaches to data enabled business and decision-making, together with the increase in high quality insight and analytics tools, it is clear that data has a pivotal role to play in enhancing the prospects of commercial and organisational success.

However, this won’t happen in isolation and without the skills necessary to wrangle, govern and exploit the data, we’re left with little more than an IT enabled filing cabinet. But what of these skills?

Can we meet the demand? The current shortage of skills globally demonstrates that business as-usual strategies won’t satisfy the growing need. If we are to unlock the promise and potential of data and all the technologies that depend on it, employers and educators will have to transform. But so do candidates, and that’s where we at MBN come in.

Placements for Data Scientists to stimulate the economy

Late last year, MBN Solutions competed in open tender and were selected as partners by Scotland’s Data Lab to deliver their MSc. Placement Project – a project specifically focused on looking at the intersection of the skills needed by business, by candidate data scientists and those taught by academia.

The Data Lab is a government-funded Innovation Centre. Its core mission is to generate significant economic, social and scientific value from data. As part of this mission, the Data Lab funds eligible students through their MSc. Courses at a variety of Scottish Universities. These courses are handpicked as representing the pinnacle of data science technical and related business skills to help prepare students for real world assignments. As part of these courses, students are offered the opportunity to participate in 10 to 12 week-long Industrial Placements – giving them real-time and real life exposure to commercial Data Science challenges at a variety of Scottish businesses.

Having placed nearly 50 such data scientists, we’re now in a position to reflect on our experiences here and what that means for Employers, Data Scientists and for the Academic realm of Data Science.

Why is this happening in Scotland?

Over the course of the last decade, Scotland has become a data science ‘Tour de Force’ and with the Data Lab helping to push this even further, it should come as no surprise that businesses located in Scotland are benefitting from the availability of high quality, fit for purpose Data Scientists. 

The purpose of this exercise, backed by the Data Lab, was to ensure that the next generation of Data Scientists would benefit Scotland even more, by being equipped with commercial knowledge and employability skills to complement their deep technical competencies.

MBN, has built a robust global reputation of thought leadership in and around all things data science, analytics and big data. So, we ensured that the 50 Data Scientists placed had the very best prospects of a great match, real opportunities to add value to their host organisations and the real prospect of developing soft and employability skills to enhance their future employment chances.

But success, of course, relied on the effective partnership of employers, educators & the data science students themselves. So, what has each constituency learnt from the experience?

An employers perspective on Data Scientist 2.0 placements

Well, we suspect there will be few surprises here, but what employers really want are skills, and not just qualifications. For these skills, many are actually in the area of soft skills such as interpersonal and team based competencies mixed with a commercial readiness to work in the labour market. Skills to facilitate greater collaboration and appreciation for business strategy and not just deep technical skills, are most frequently cited as key to the success of a data scientist in post. Perhaps version 2.0 of the Data Scientist is likely to be more rounded and ready to work alongside key data enabled staff and management of the organisation in which they are employed?

But it’s not all one way here. What we found was that there was a need for businesses and other organisations to finesse or modify their approach to finding Data Scientist 2.0.

Of key importance here is that the organisation is able to provide great clarity of purpose. Not just a vague job description but signals and indicators for academics and data scientists alike to use to help them navigate towards a more effective curriculum and better equipped staff.

It’s also about understanding actual role descriptions. Data Science is a broad term – Is this what you really need? Or do you actually need an Insight Analyst? Either way, building structure into your talent plan for any organisation should help you to understand what you actually need and how it might fit in with the rest of the workforce. Building a modern business and its associated workforce in a connected economy need real thought as to how the jigsaw must be pieced together.

Perhaps even more importantly here, is that it will help you to understand gaps which can be used to craft learning and development activities for data science staff when they join the organisation. Think about this not just as an opportunity to equip the staff with employability skills but as a mechanism to enhance organisational collaboration between those with deep data science skills and knowledge and those with business and domain expertise to deliver inclusive lifelong learning for all staff.

Educators perspective on Data Scientist 2.0 evolution

For the academic community and, based upon our experience of placing 50 of the most suitably qualified individuals into leading organisations, we feel that Higher Education needs to continue to focus course development to help break down the data science silos and instead facilitate multidisciplinary strength.

Our view is that Scottish Universities perform particularly well here, but there is always more that can be done through the process of office and business simulation, business placements and internships such as those offered by the Data Lab and by encouraging greater collaboration between businesses and the academics running the courses.

For non-data science courses, it is key for universities and business schools to help champion robust data literacy for all students since all aspects of their future work and life are likely to be impacted by data science in some way, shape or form.

From our experience, some of the issues faced by organisations themselves getting the best from their data scientists boils down to their own data literacy… perhaps Schools and further education have a role here to play in enabling all students to become data literate and open to more non-traditional routes to data science – Once again, many in the world of academia could take a lead from the Scottish Universities as we felt that they, with the Data Lab, set clear intentions and outcomes for the MSc placement project that were a fantastic exemplar of what needs to become part of the fabric of higher education. In particular, building more commercial and professional ties with the business communities and strengthening alignment of the academic agenda to the unmet business need.

Robin Huggins who managed the programme for MBN, talks extensively on the diversity of the data scientists leaving higher education, but it is key that such diversity is still not quite where it needs to be. Higher Education needs to take a collaborative approach with industry to ensure that their plans for recruitment of both students and staff materially contributes to a diverse analytics workforce for the future.

What did the Data Scientist 2.0 students think?

We found that for many, this seemingly daunting activity of being placed with an organisation was compounded by the gap between academia and business. More evident to us as we work on the cusp of both, for candidate Data Scientists, language, timings, terminologies and techniques are different for those working and studying within Universities and those working in industry.

With the right will and collaborative minds, this can be readily overcome. We found hundreds of people – students, academics, HR practitioners, technology professionals, data people within Industry and founders of businesses with the collaborative trait and this made the process much easier. But if you’re not so lucky, it could result in the square peg in a round hole syndrome.

What could you learn from these findings?

Have you experimented with placements for Data Science students? If so, I’d love to hear your experience and whether or not it accords with what these Scottish teams found.

In part 2, Michael will share how employers can find Data Scientist 2.0s and what to do if you currently are a Data Science student. Plus, he’ll explain more about the role MBN plays and why that business model is working for them.

Feel free to add any questions in our comments – I’ll fire them straight at Michael!