Data Scientists 2.0, you’ve heard the case, now what can you do about it?

Following on from guest blogger Michael Young’s first post on Data Scientists 2.0, this one shares next steps.

MBN chairman, Paul Forrest, builds on what Michael revealed as the learning points for employers, universities & students (from Data Science placements in Scotland).

In this shorter post, Paul considers key questions for both employers and students: How can employers find these new (improved) Data Scientists 2.0? How can students currently studying Data Science become this new improved candidate.

Plus, Paul explains the role his firm plays in this match making.

Over to our latest guest blogger, to complete this two-part story…

How can employers find their own Data Scientists 2.0?

The starting point is to think about your requirements and how you will frame these. Don’t just think about academic qualifications, think about the soft or interpersonal skills you need. This is about how these are pieced together across the organisation’s entire talent management plan and identify skill levels required as ‘must have’ or ‘desirable’.

Think laterally, many companies experience great success with candidates with some exposure to the arts and humanities as it often teaches students to approach complex challenges using critical thinking and problem solving, creativity, communication, and collaboration. These creative thinkers have what management say they will want long after some of the coding and data science tasks we know today are automated through available tools, artificial intelligence and machine learning.

Be open-minded to the social and cultural backgrounds of candidates. It’s all too easy to be unintentionally biased to recruiting the usual suspects who look, sound and act like the interviewer. Avoiding bias to gender, ethnicity, social background and possibly even the country of study will undoubtedly help find a wider gene pool of talented, more diverse candidates.

Hackathons, coding competitions and trial data model builds fall into the category of competition and these can be used effectively to identify the very best candidates. But don’t stop there. Why not hold situational analysis or critical thinking exercises as part of the competition? Use these to find those elusive, more rounded individuals with deep coding, data science and analytical skills with a thick veneer of soft skills and employment competencies to wrap their technical expertise.

Finding those rare recruiters who can sit across the board between business and academia is a useful starting point, but think on… we also craft opportunities for the very best employers to ‘try before they buy’ with the very best candidates by building programmes such as that we built and operated for the Data Lab. Run an in-house version and find suitable talented people to join your team. The benefits are huge as you get the opportunity to mentor and shape the individuals approach to entering the labour marketplace. You also have the opportunity to collaborate and determine the extent to which you can build a learning and development wrap to enhance employability before making the critical employment decision. This is a classic, win-win as the candidate data scientist receives feedback and guidance on developing necessary commercial, interpersonal and soft skills and the business has the opportunity to find people where there is a complimentary fit on a low risk model.

What about meetups and data science events? These are regular sessions run across the country, many by us, designed to allow people to learn together and socialise to understand each other’s perspectives on what they have to offer and how there may be a strong and complimentary fit.

I’m studying Data Science at University, how do I become one of these Data Scientists 2.0?

Well aside from the obvious attendance at meetups and data science events, to ensure you are networking and socializing with the right kind of future employers, there is much that can be pursued here. MOOCs (massive open online courses) from the likes of Edx, FutureLearn and Coursera are a great source of soft skills and employment competency training. They are flexible and cheap/free and can be wrapped around other activities. Courses here allow for rapid acquisition of business and domain expertise and knowledge that will at least facilitate you holding your own.

Volunteering and internships also offer great opportunities for you to learn more and acquire employability skills, it’s also a great way to enhance your own network but real traction here comes by searching out internships and opportunities to spend time with future employers.

Finally here, one of the most innovative methods of showing it’s not just about your data science skills, is a recently experienced method of the candidate concerned positioning themselves as someone who could communicate in the same lexicon used by the business. A short thought leadership piece together with a schema for how predictive analytics may help with understanding some of the new country entry issues faced by the target business was enough to secure the candidate an interview with a key business team leader.

The fact that the thought leadership, once drafted, was in the same type of language used by the business itself helped illustrate the employability of the candidate concerned. Then, one assessment centre later, the Data Scientist concerned secured a role in the new ventures team at this particular FTSE 250 business without it ever being advertised!

The role of recruitment agencies should play

Clearly, much of what we are describing here came about as a result of the MBN role in this exercise for the Data Lab. We had to figure it out, determine any gaps and help build the bridge we so frequently talk about. We were the ones who helped the Candidates understand the specific organisational unmet needs of the target businesses. We were also the ones who finessed the soft skills and employability work done by the Universities with training delivered by Robin Huggins to help the candidates to become sufficiently ready for their roles with the 50 businesses concerned.

But this, for us, is just an example of where businesses such as ours, should be going the extra mile for both their clients and the candidates. Facilitating deep and long-term relationships means happier clients and happier candidates – both of which deliver tremendous benefits all round.

Conclusions

So, well it was a great learning experience for MBN Solutions as Robin Huggins and his team worked their way through placing 50 Data Scientists with Scottish Businesses. With all this in mind, it is clear to us that employers are seeking technical and domain expertise as traditionally expected. But, and it’s an important but, they want these in the context of the ability to apply the skills to real commercial and organisational issues and backed by hands-on experience, evidenced through apprenticeships, internships and placements such as those delivered on this project. They also want stronger, more obvious collaboration and influence by the commercial sector on the academic agenda, and where the resultant candidate workforce has more diversity, inclusiveness and is non-exclusive.

Thanks, Paul. Some great practical experience there and a useful additional perspective to our normal content.

If you’d like to find out more about the support/education MBN can offer, you can reach them here.

I’d also be interested in hearing what has worked for you. Have other organisations made a difference to you being able to hire the Data Scientists you need?

Are you convinced by the case for Data Scientists 2.0?