Having started out his career in consulting, David Nazareth knew that he wanted to transition into a data science role in the renewable energy sector. Having struggled to find a masters programme that offered the right blend of data science and management, David discovered London Business School’s Masters in Analytics and Management (MAM) programme and the rest, as they say, is history.
I grew up in Bangalore in South India. I studied mechanical engineering at the National Institute of Technology Karnataka for my undergraduate degree, and after graduating I wasn’t really sure what I wanted to do. I knew I wanted to get a bit of real world experience, so I went into consulting, as it offers you the chance to work with clients across different industries.
I worked as a Business Analyst at Deloitte India for two years. Those were quite influential years for me as I did a lot of reading about things that were happening around the world and in different sectors and industries, and I became interested in climate change and the problems it will cause for us in the future. So I finally decided to focus my efforts and target the renewable energy industry. I was also becoming interested in data science at the time, so I decided to look for a masters programme that would enable me to work in the renewable energy sector in a data science role.
The MAM programme at London Business School stood out to me because it was a good blend of data science and management, which I couldn’t find in any other programmes I researched. I’d learned how to write code during my undergraduate degree and I had some programming skills, but I didn’t have the rigorous background in statistics or data visualisation that I needed, and I didn’t know anything about machine learning.
As well as the programme, I was attracted to the School because it has a really strong network of alumni. I wanted to tap into that in order to get into the renewable energy sector, and because I was transitioning from the consulting sector to one where I didn’t have any experience, I thought having a network would help. I also wanted to study in the UK because it has one of the largest growing renewable energy sectors, especially in wind energy, and I was interested in working in that space in the future.
During my time at the School, I really enjoyed the modules in applied statistics and data visualisation, because they were really well designed and taught. I also liked the module on decision analytics and modelling. Those three really stood out to me among the data science electives, and I liked that some of the assignments were grounded in real life work that our professors had undertaken. In terms of management modules, I really enjoyed marketing, economics and operations management. I also took an elective on energy markets models and strategies because I thought it would be useful; it taught me quite a few things that I don’t think I could have learned by myself, because it was an amalgamation of research from quite a few different areas.
I also completed a LondonLAB project with National Grid. LondonLAB is a ten-week student group consulting project with a real client. The challenge was to predict the carbon intensity in various European countries five years into the future, and it was much harder than it sounds. Some of the data collected couldn’t really be used so we had to spend a lot of time acquiring new data, cleaning it and putting it into an appropriate format so we could use it to solve the problem. That’s very similar to a lot of situations in the industry, because the data available in any company is never perfect and always needs to be cleaned or formatted before you can actually use it. That was a big learning that I took from that project.
Building my network during the MAM was great, because we had a lot of opportunities to pair up with classmates on different projects. I had to do a couple of assignments with my study group for almost every course, so that gave me a chance to really learn how to work with people from different countries and different backgrounds, and solve problems in a more diverse setting than I was used to.
I also got a lot of exposure through the events organised by the clubs, especially the Energy and Environment Club, which puts on a Global Energy Summit every year. I was a member of the Energy and Environment Club and attended a lot of their events. This gave me exposure to some really interesting thought leaders in the space of renewable energy and I was able to get a few contacts that I could reach out to and follow their work. It really helped to learn more about the industry and eventually secure a job working in the sector. I’ve even continued to attend the events since graduating, including this year’s Global Energy Summit.
The diversity of the programme was great. There were people from 29 different countries in my class alone. Each study group was also really well crafted, with a mix of people from different countries with different skillsets and different levels of exposure to programming, data science and management, so everyone complemented each other. This was beneficial because everyone had a different perspective having been brought up in different parts of the world, and everyone had learned things slightly differently. Solving problems together with people from those different settings was a huge learning opportunity.
The support offered by Career Centre is one of the best parts of the programme. They do a tremendous job of getting you ready for the recruitment season. Before we joined the School we were asked to prepare our CVs and cover letters to be reviewed by the career coaches, and we could book in one-on-ones with them. They advised me and really helped me to prep for my desired role. I was trying to apply for data science roles in the energy sector, which is a very niche role in the current market, but they really encouraged me and kept my confidence up. They also work with sector leads across various industries, and they really know what’s going on and what recruiters are looking for. They also provide you with resources to study before your interviews, so I made good use of those. That was one of the things that helped me to really prepare for interviews and know about the companies I was applying to.
I’m now working as a Data Technology Graduate at EDF. It’s a two-year rotational role that involves moving between data science teams across EDF’s customer’s business. My first rotation was in the data science team within sales and marketing, building data science products to help our customers save energy or manage their bills. Some of the problems are really hard, and it can be quite a challenge to collect data, assemble it and figure out an approach to building a model that can actually solve our customer’s problems. But I like it and it’s been really rewarding, especially in knowing that I can leverage some of my learnings from the School towards solving some of the problems that our customers are facing.
The MAM programme has been really helpful in my role, as a lot of what I was taught translates to my day-to-day work. The blend of management and analytics taught me to look at each data science course from a business perspective and understand exactly how data science adds value to the business problem at hand. Even now, when I come across something new, the MAM programme taught me how to quickly learn a concept and apply it to the real world, so that was extremely useful. During the programme you do so many modules in a short space of time and it improves your ability to learn quickly, which has also been really useful in my job, as I have to learn a lot of different technologies and concepts, and apply them to solving problems.
I want to stay working in the clean energy industry for now, but in the long-term I’d like to move into a role that relates to climate change research. I’d like to study the effects of climate change and use data to report on it. In the long-term, I think the connections I made at London Business School will be the most valuable aspect of my time there, as I’ll always have a network that I can tap into and reach out to if I need it.