"Keep Learning" - Reuben Sodhi, Data Scientist at Frame

Have you ever considered a career in Data Science? Reuben Sodhi, Data Scientist at Frame, gives us the low down on his career so far, and shares his helpful advice on how to secure a job in this exciting industry!

Rueben Sodhi, Data Scientist

Hi Reuben! Thanks for speaking with us. Tell us about yourself - what's your background and how have you progressed your career?

I’m currently working as a Data Scientist for Frame, a Data Science and Machine Learning consulting start-up based just outside of Huddersfield. We help clients solve problems using Data Science and develop our own E-commerce based products. I’ve been working here for roughly a year and a half and in that time I’ve gained some amazing experience in the Data Science industry working as part of a small but solid team of eight people. This is my first full-time job and previous to this role I completed an internship and work experience placements in the Finance industry. As for my academic education, I have a BSc in Mathematics and an MSc in Data Science and Analytics. I began this role as a Junior Data Scientist with a good analytical foundation but little to no understanding of how Data Science is used in business. By working on projects at Frame I have attained an understanding of the field on a commercial level and how Data Science techniques are leveraged on a production scale.

What drew you to a career as a Data Scientist?

I have to admit that my career in Data Science wasn’t completely planned. After I completed my undergraduate degree my plan was to start looking for jobs in the Finance industry. However, this also happened to be the time when the Covid pandemic hit so I decided to continue studying and apply for a Master’s degree. After some research, I came across the field of Data Science and Machine Learning. The first thing that really struck me was how many industries this field spans across, from Finance and Big Tech all the way to Healthcare and Retail. For me, this really indicated that Data Science is a vital and in-demand profession that has wide applicability to different business sectors. But what really sealed the deal for me was researching what a career in Data Science entails. I was really drawn to the concept of being someone with a wide range of tools under their belt, such as machine learning or mathematical modelling, and having to figure out which tool best suits the problem at hand.

What excites you about your industry, now and in the future?

There are many things that excite me about the Data Science industry but something that never fails to amaze me is the speed of innovation in this industry. It seems like almost every week I read about a new innovation made using Data Science or Machine Learning that solves a problem previously thought to be incredibly challenging. A very topical example of this is ChatGPT which is already beginning to make incredibly advanced search tools used previously, such as Google, look obsolete. This rapid innovation also has some very encouraging consequences going into the future. Data Science and Machine Learning are being used already to solve some of the major issues facing the world. Some examples include using computer vision to improve automated recycling efficiency rates and hence reduce plastic pollution, and also using machine learning to automate the drug research and discovery process which will increase the turnover rate of life-changing therapeutic drugs. In this sense, it is very exciting to be a part of an industry that has the potential to make positive contributions to the world.

What advice would you give to someone wanting to break into Data Science?

One thing you can do that I believe has as much, or even more, importance than getting a university degree is completing some Data Science projects in your spare time and saving these projects in your own public GitHub repository. This means that anyone who has a link to your repo can get hands-on with code you have written and see the results it has produced. Choosing an area of interest that suits you and working on a project based in this area is also a really good way to develop a deeper understanding of Data Science while working on something that you enjoy. It can be difficult to think of a project from scratch so I would also suggest having a look on Kaggle or YouTube if you're stuck for project ideas. Completing your own projects in your spare time is one of the best ways to show employers that you are enthusiastic about this subject and motivated to pursue a career in Data Science.

What are your top 5 Tips for thriving in a Data Science role:

To wrap things up here are my top five tips for succeeding in a Data Science role:

1. Keep learning: because this is such a rapidly developing industry there will always be something new to learn, so stay inquisitive!
2. Don’t be intimidated by difficult projects: quite often, I’ve had to work on projects that seem daunting and complicated at first, but these are normally the ones where I learn the most and look back on with a sense of accomplishment
3. More complicated does not always mean better: although complex machine learning tools are often the answer, sometimes a simple approach will do the trick, and you will save yourself a lot of time and effort by choosing a more simple tool
4. Develop good interpersonal skills: this might not appear to go hand in hand with Data Science, but being able to effectively communicate your work with others, whether that be a colleague or a stakeholder, is an invaluable skill
5. Attend technology networking events: attending networking events and meetups will expose you to interesting and talented people outside of your normal circle and help you develop into a well-rounded professional

Thank you for taking the time to read about my experiences working as a Data Scientist and I wish any aspiring Data Scientists the best of luck.

Thank you Reuben! If you're interested in a Data Science role, check out all of our tech and analytics opportunities today!

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