Sonia Ingram

"Developer - Explorer - Quality ControllerI'm pretty surprised at how well the MSML results describe me!Developers are described as being creative problem solvers who are good at understanding others and want to improve the world around them. I am super happy to be described as a developer as all of these qualities are essential to be a good data scientist! You need to use creative problem solving to think about a business problem in lots of different ways and think outside the box of how you can solve them. You also need to be good at understanding others because data scientists have to deal with both business people and technical people, and be able to understand a business problem from all different points of view. You also need to be able to guide non-technical people through the world of data, so good communication is essential. Finally, improving the world around me is something I am truly passionate about- AI is a scary thing for a lot of people, but if we can harness its power for good and make all data science ethical and beneficial we're onto a winner!Explorers are described as curious, eager to learn, practical and good at researching/ experimenting. Again, all great qualities for a data scientist! Data science is such a fast moving field that new tools and techniques are being developed all the time. On top of that there are loads of different roles that come under the umbrella term 'data scientist', and it's impossible to be an expert in all of them so you need to be eager to learn new things, happy to research the different ways to do something, and then happy to experiment with all of the different options.Quality Controllers are described as trustworthy, with a natural sense of justice, good with detail, fair, honest and ethical. I am very proud to have been given this personality type! As I mentioned before I am a big advocate of ethical data science and using AI to change the world for good. I think these are really key qualities to be a great data scientist. A lot of data science products can have a huge impact on peoples lives, so it's crucial that these products are built the right way so that no one is accidentally harmed by them. "

"I am a data scientist. Data science is a really new field, so lots of people can be called data scientists but have quite different roles! The thing that brings all of these roles together as 'data science' is that you're using data to understand or solve a problem.As a data science consultant, my job is to work with different businesses to help them get the most value from data they have, and to use this data to solve some kind of business problem.These problems can be really varied as we work with lots of different sectors (from healthcare to banking), and each business can have completely different problems that need solving.The main things I do for my job are writing code to either analyse data, or to create 'models'. Data modelling is a catch-all term to describe using some kind of algorithm on data to do a thing (for example to predict future sales, or to classify whether or not a photo contains a cat). Alongside coding, I have a lot of meetings with my team to discuss what we are working towards and what we have left to do, and also meeting with the business areas we are working with to update them on how things are going.The things I enjoy most about my job are being able to use my brain to do something that is actually going to change the way a business works. Depending on the project, this can have a big impact on real people. I also love working as part of a team and being able to constantly learn new things.The starting salary for a data scientist depends on your experience, but is usually £30,000-40,000."

"I did not take a straightforward route into data science! I took Biology, Chemistry and French for my A-Levels and went on to study Pharmacology (drug development) at uni. After uni I moved to Australia for a year and worked as an administrator on a construction site in the middle of the desert (it was a lot of fun and I was able to save up to go travelling around South-East Asia before coming back to the UK!) Once back in the UK I worked in Medical Communications and PR for almost 2 years. I didn't really enjoy it though and I missed science and learning things, so I decided to go back to uni and completed a Masters in Transfusion and Transplantation Science. I enjoyed that so much that I decided to apply for a PhD in Immunology and Molecular Biology. My PhD made me realise that I didn't want to work in a lab full time, but that I loved experimentation, statistics, and analysing results. This gave me the idea of moving into Computational Biology, which is using computers to model things like protein interactions. I started going to different talks about Computational Biology, and through these I met some data scientists. After hearing about data science I knew that this was what I wanted to do! It brought the experimental/analytical side from my PhD together with the business/consultancy side of working in Medical Communications.I applied for different bootcamps in London aimed at people with PhDs who want to move into data science, and I undertook an internship with BJSS so I could see what it was really like to work in this space. On the back of the internship I was offered a job at BJSS, and I completed the bootcamp a month or so afterwards which was great! I have now been at BJSS for just over two years and I've loved it."

"I am a very outdoorsy person. I love going camping, hiking, cycling, rock climbing, snowboarding, scuba diving- you name it! I feel like people who work in STEM have this stereotype of being a bit nerdy, but in reality working in tech is cool and you find people with all different kinds of interests.I have also recently adopted a very naughty beagle called Barney. :D"

"What I get up to each day really depends on what stage the project I am working on is at. If it's very early stage I would be having lots of meetings with the business areas that are interested in working with us to create a data science solution. These meetings would be to help me and my team understand what the business needs, how things are currently being done, what needs improving, and what data is available to use. I would also start doing some data exploration and small scale experiments with their data to see what we can reasonably achieve, and which kind of approach is best suited to the problem we are trying to solve.The middle stages of a project are all about developing the real data science solution which is going to be used in the business. This stage will be mostly about coding and developing the model and its surrounding infrastructure. I would have regular update meetings with my team and the business area to make sure we are on track and to iron out any problems.The final stages of the project are all about 'going live'. This stage often needs lots of help from other teams (software architects, developers, and testers), who will all work together to try to plumb the new data science solution into existing software architecture and make sure it's as robust as possible. At this stage there is also lots of writing to document the solution and make sure that everyone understands how it works and what risks are involved with putting it live."

"A-Levels in French, Biology and ChemistryBSc in PharmacologyMSc in Transplantation and Transfusion SciencePhD in Immuology and Molecular BiologyBootcamp in data science"