5 Top Tips for emerging Data Scientists & for Companies

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MBN’s director of Academy & Client Services, Robin Huggins, tells us his top 5 tips for both emerging Data Scientists and Companies.

The world is changing and so are our expectations of what it means to be work ready. I spend a large amount of my professional time working with Graduate Data Scientists and the organisations that are looking to provide the very best opportunities for this “in demand” Talent stream.

I’ve been asked, on many occasions, for advice from both employers and candidates alike around the specific challenge of transitioning between Academia and Industry.

So… to help those considering this, I’ve set out below what I consider to be the “Top 5 Tips” for both emerging Data Scientists and Companies looking to be viewed as “Employers of Choice for Graduate Data Scientists”


1. Focus on a discipline.

Data Science is a huge field. No-one (in particular at entry level!) is expected to know everything. Choose the discipline (ML, Visualisation, Analytics, etc) that you most enjoy and/or excel at – and focus all on securing employment that allows you to upskill in this area quickly. The more you do, the more you’ll learn. A focused discipline approach will allow you to practice in an area you understand intuitively, freeing up thinking time to learn new approaches/techniques, etc

2. Immerse yourself in knowledge.

You have to consider that in the fast moving domain of Data Science, you are unlikely to EVER be finished with learning. In fact, many Data Science Industry experts are of the view that learning only really begins when (full time) Academia ends. Continue to absorb everything you can regarding new advances in the field, research papers, approaches, challenges, etc – expect to be asked for your opinions on areas “out with” your chosen discipline. You will be viewed as a “Data Science expert” within any organisation, so a broad understanding of the (wider) industry is essential

3. Understand Business.

You will transition far more rapidly into commercial organisations, and progress faster within them, if you “get” what the business is about. Understand structure, hierarchies, products, processes, people and politics (with a small ‘p’). You never know when your Data Science skills will be required by someone from a different part of the business to the area that you are normally allied with – so make sure you can quickly absorb “domain expertise” at the same pace as you absorb “discipline” expertise and broader “Data Science” expertise. The most effective Data Scientists are “Business Data Scientists” – without an engrained understanding of how business works, how can you realistically add value through Data Science?

4. Work on your Soft Skills.

Even with “business understanding” you will find your transition to “business Data Scientist” difficult if you cannot engage with “less-technical” colleagues. People who work within Sales, HR, Product, Marketing and Finance teams will have very little (if indeed any) understanding of the nuances of your profession, and will expect you to be able to explain what you do in a clear and understandable manner. Learn how to negotiate, present, collaborate, share and consult. Listen carefully to the needs of your less-technical colleagues. Ensure you maintain an “approachable” persona throughout.

5. Build and maintain a network.

Keep in touch with former student colleagues, Course Leaders, researchers, academics and anyone who has been part of your “learning journey”. Expect to regularly contact them to ask for advice, guidance, explanations and support. Data Science, as a field, has a strong connection to Academia so ensure that you nurture and cultivate the relationships that you have built on a long-term basis. Once you start working commercially, maintain exactly the same approach to cultivating your professional networks. You never know when you will need to ask for help or advice so make sure your “little black book” is kept up to date and used frequently!


1. Be flexible.

A job specification or role profile is your “wish list”. Don’t be overly fixated on individuals at an entry level “ticking every box”. Make “hiring on attitude” a key component of your selection criteria. An applicant with the desire to learn, the willingness to undertake training and the aptitude to absorb new ideas quickly can often be brought up to speed with any areas of experience that they are lacking in a short timeframe. Remember, academic courses are often designed to attract student applicants in the first instance – sometimes the needs of industry are secondary to this purpose. Take a flexible, holistic view of a Graduate candidate’s experience and, very often, you will be rewarded.

2. Focus on the applicant experience.

We work in an industry that measures and promotes User and Customer experience as key metrics. Ensure anyone engaging your organisation in the capacity of Applicant has a memorable, pleasurable experience – no matter the outcome of their application. Make the Applicant Experience a key metric for everyone involved in the hiring process. News travels fast – bad news even faster. Ensure that your organisation receives the right type of publicity for the way they engage emerging Data Science talent and use this to help become the “Employer of Choice”.

3. Partner with Academia

Build relationships with the Universities where Data Science courses best mirror the needs of your business. There are a huge variety of ways in which Academic engagement can be realised by organisations: Advisory Boards, collaborative projects, formal placements and internships, guest lectures and presentations, hackathons and Meet-Ups. If you want to ensure that Graduate Data Science applicants are easily transitioned into your business, don’t wait for them to Graduate to influence their course of travel.

4. Choose your technology wisely

Remuneration and reward is only a cornerstone driver that Graduate Data Scientists will look to when choosing their “Employer of Choice”. Access to technology stacks that allow them to continue their learning journey, work on the latest tools and techniques and augment their existing skills are often key factors when Graduate Data Scientists make decisions on the organisation they wish to join after their studies have concluded. Have an open mind when it comes to the balance between organisational delivery and appealing to the needs of fresh Data Science talent. The ability to “play with the newest toys” can often influence decisions in your favour when several opportunities are being considered.

5. Have interesting work

It goes without saying, but the more “interesting” the work that Graduate Data Scientists can get involved in early in their new role, the more likely they will be to want to work for you. Someone who has spent many years understanding the underlying mathematical principles of Deep Learning may not be too happy if the work they are asked to do doesn’t draw upon this knowledge, or if the tasks or functions you ask them to carry out are not of interest or stimulating to them. Employ the “buddy” system wherever possible, where experienced mentors can supervise Graduate talent as they undertake challenging, but interesting, work.

For candidates and employers alike, if all of this seems a little too regimented, then start out by attending better known Meet-Ups. These are a genuine safe haven where you can learn from those who have gone before you.

If all else fails, feel free to contact me with your questions!

Robin Huggins | Director of Academy & Client Services
DD:  0141 225 0134 | E: robin@mbnsolutions.com