How did you first get into data science?
My first encounter with data science started when I was studying for my first degree on Operations Research at university. Back then I studied a comprehensive range of theories and techniques including optimisation and game theory. This has led me into the field of AI and data science where I have conducted further research in fuzzy logic, genetics algorithms and artificial neural networks. As part of my PhD thesis, I have developed frameworks and novel AI/ data science techniques in investment decisions, dealing with uncertainty in project evaluation.
When I finished my studies, I started working for the energy sector where I held several roles including commercial and strategic roles. Even though these roles were not formal data scientist roles however, because of my background I was able to apply data science to create value for the business. Nowadays, I lead Mind Senses Global, which I founded to help businesses and organisations apply artificial intelligence and data science.
Data science creates value for business in several areas: to reduce costs, enhance margins and improve customer satisfaction. One area of the business that has significantly benefited from data science is sales & marketing. Data science is being used to improve targeted advertising and content recommendation by clustering customer profiles and preferences. Once these preferences are identified, businesses can tailor their offering. Data science is also used to analyse customer profiles data, their purchase power and product specifications. The outcome of the analysis sets the pricing strategy.
What are the top challenges you currently face as a professional data scientist, and how do you go about tackling them?
One of the top challenges I encounter when helping clients apply data science is data. Availability and quality of data are key to the successful application of data science. We always advice to have a robust AI/ data science strategy in place that addresses several factors including data and whether the business has the right infrastructure to collect the right data.
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The second challenge is the hype around AI and data science and what they can deliver in practice. This aspect creates confusion when it comes to applying data science in practice. To address this challenge, we educate our clients on AI and data science and we always urge them to start with the business problem first and to view AI and data science as “a means to an end”.
How important is the domain knowledge of the business/industry you’re in as a data scientist, and how did you acquire it?
Domain knowledge is key when it comes to applying data science. For example, if we are building models for the banking industry, we will need knowledge on banking and financial services to be able to build models correctly and more importantly to be able to train and test them. Similarly, we have seen recent applications of data science to fight covid 19, these models would not be able to provide good quality results without the involvement of scientists who understand the science behind pandemics.
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The business domain knowledge could either be acquired by collaborating with experts within these fields or by working in a business sector. I have domain knowledge of the energy sector as I have worked for energy companies in the past and my team members at Mind Senses Global have different domain knowledge such as marketing, manufacturing, and financial services.
Do you create data science content?
Yes, I create various data science and AI contents. There is a masterclass that I deliver under Mind Senses Global AI education services, which is titled: “Unlock AI Potential in Business”.
The workshop is designed to equip participants with practical knowledge on AI within a business context and how to unlock its full potential. The masterclass investigates how data science drives value for the business, how to formulate AI strategies and how to address data science challenges. Participants will also examine how data science has been applied in different sectors of the economy and how to identify the traits of a successful AI application/ opportunity.
What advice would you give to someone who wants to get into data science today?
My advice is to get a good understanding of the mathematics and statistics behind the algorithms that are used in data science. I would also suggest to anyone interested in data science to start building a portfolio of projects. There are a lot of opportunities to get involved in data science projects such as through Kaggle competitions or GitHub projects or Omdena platform that builds AI solutions for real-world problems.
Your favorite thing about working in data science:
My favourite thing about working in data science is the fact that you always continue to learn, each day there is a new problem and a new opportunity to solve it.