A career in data science and analytics can be quite rewarding, but the journey is not straight forward. For many professionals, their career journey starts from being accepted to a programme at their selected institution of higher learning, where they will be taught in the fundamentals of their field. After graduation, they are recruited by an organization that will give them a start in the real world of work, where they will work on real projects, serve clients, make products, offer services, and grow in their field.

Data Science is one of a few fields that have no specific trajectory; with professionals that come from various backgrounds and from all walks of life, it is not too uncommon for a person to make a career change into the data science field. Afterall, it would be difficult to find an organization or institution that does not use data or produce data. It is often said that data is the new oil, and that notion is not too far from the truth. An organization that is, not only aware of its data, but further uses that data for strategic decision-making, will not only succeed in this modern day, but will thrive.

It is no surprise that the field of Data Science and Analytics will attract many types of graduates and professionals. It does not matter what the person has studied, but what matters the most is their passion for insights and the skills that they have amassed that will allow them to unlock meaning and value out of data and produce meaningful information for informed decision-making.

Amidel, being a leader in the field of Data Science and Analytics, is often on the hunt for the best talent. During our recruitment drive, we met many candidates from various academic and professional backgrounds, and it is in this experience that has inspired us to profile some of the academic backgrounds of the candidates we welcomed into our Data Science and Analytics portfolio.

Computational & Applied Maths

Computational and Applied Maths is a valuable skill as it prepares the candidate to be able to use computational techniques and theory to build models and algorithms to solve real world problems and to use mathematics to develop commercial solutions. The candidates with this sort of background can solve complex problems in many different fields, such as financial services, engineering, sales, and manufacturing.

The Applied Maths candidate was hired, not only because of their academic background, but also because of their experience in the field and their confidence in the work that needed to be done. They would bring a strong base of mathematical modelling, which is an essential skill for any data scientist. Another quality with this candidate was their confidence in their response to interview questions and they had solid, practical experience from their previous role.

Actuarial

Actuaries excel in measuring and managing risk. They rely on their strong mathematical and analytics skills to design programs and models that help to control risk and predict behaviour. Like other data science professionals, actuaries gather data to analyse and estimate the probability and cost implications of certain events and decisions, and it is this skill that makes them valuable to financial institutions, insurance companies, and government agencies. Such predictions help these organizations to better plan for uncertainty and to price services and products in a sustainable manner.

It is not different for our Actuarial Analyst candidate who, fresh from university, already had the fundamentals in place, and had the determination and passion for data, like no other. Their willingness to learn and contribute, set them apart from other candidates, proving that data scientists do not only need the right educational background, but also require a certain level of determination that will carry them even through the most challenging periods during their quest of unlocking insights and delivering value to our clients.

Civil Engineering

Civil engineers are trained to be able to plan, design and oversee construction and maintenance projects of various sizes. They can run projects as large as railroads, highways, skyscrapers, dams, airports, powerplants, etc. Civil engineers need to be good at problem-solving, have a solid mathematical and IT background, and be excellent project managers. It might not seem like an obvious route to take to become a data professional, but those mathematical and analytical-thinking skills are enough to kick-start a data science career, especially combined with an information systems background.

We were very much impressed by our candidate that came from a civil engineering background, because of their self-starter attitude and entrepreneurial drive to solving problems. This candidate would become a great addition to the Amidel team, as we are passionate about solving problems and answering business questions for our clients in the most creative ways.

Physics

With Physics as a major, these candidates make great researchers and are good at coming up with new technologies and processes to solve problems through experimentation. They are good with numbers, are analytical and scientists at heart. A physics background means the candidate is keen on discovering new facts about the world around them and it is this curiosity that makes a good data analyst and data scientist.

Hiring a candidate from a physics background, without much experience, was a pleasant learning curve for Amidel. When the candidate was given tasks to perform, they persevered and were able to deliver the expected results after going out and seeking the information and educational sources available at their disposal. Even though Amidel fosters the culture of learning and the furthering of one’s knowledge in their field, this quality of self-learning and research, gave this candidate the edge over other applicants, because it shows there is no limit to their growth potential.

Conclusion

Through this recruitment drive, Amidel was able to learn more about the different backgrounds that may eventually lead to Data Science and Analytics, with some candidates really challenging us to look beyond titles and the typical data-oriented academic degrees and focus more on the skillset of each individual that sets them apart and makes them the best data science professional. Amidel, boasts a diverse skillset and it places us at a unique advantage to be able to answer the most complex analytics questions for our clients, but to also build excellent technological platforms that solve the most complex and diverse business problems. The success of these interviews and the whole recruitment process, really shows that, truly, all roads can lead to a rewarding career in the field of data science.