I joined the Civil Service Fast Stream graduate programme as a statistician. I was seconded out to NHS England, where I worked on research, statistics and econometrics related to pricing models in the NHS.
Policy Advisor - Venture Capital, HM Treasury
I was in charge of policy for the government's venture capital tax relief schemes, which provide over £2.4bn of investment to small businesses every year. I worked on research and analysis, did stakeholder outreach with the heads of big investment companies, represented the UK at an EU working group, and had my policy decisions made into UK law.
Pricing Analyst, Lavanda
I joined a property technology startup as a pricing analyst. The company managed about 400 Airbnb properties, and I was responsible for making sure they were priced correctly to keep occupancy at the right level. I also set up the company's reporting and dashboard systems from scratch.
Head of Revenue, Lavanda
I was promoted in my company to lead the analysis and revenue team. My team was responsible for anything to do with data and analysis - pricing our properties correctly, producing research reports for some of the UK's biggest estate agents, improving a lot of our internal processes, and making sure my team had training in the analytical skills they needed.
Senior Product Manager - Data, Lavanda
I was promoted again in my company, to continue to lead the analysis and revenue team and also to be responsible for all of our data products. I designed, managed and delivered products that made use of data and analyses, including an app to predict the amount that any property in the UK would make on Airbnb.
Data Scientist - Citymapper
Citymapper is the world's best travel app. I used machine learning and statistical analysis to help us understand our users better, like predicting which users might want to sign up for Citymapper Pass (like a better Oyster card for travelling around London). I also helped come up with ideas for new smart features in the app that are built using data science.
I work in Prime Video, using data science and machine learning to do things like predict how many people will watch our TV shows and movies, so we can make sure we get the best possible selection of things for our customers to watch.
I love my current job as a data scientist! It's awesome getting to use technical skills like maths, statistics and coding to have a positive impact on real people's lives, like making sure we have the best possible TV shows and movies. Plus it's great to see people's reactions when you say you work at Amazon!
I also really enjoyed working at the Treasury - you get a lot of responsibility there, and I got to go to Brussels to represent the UK at a meeting full of senior civil servants from every EU country. Definitely a career highlight so far!
Another career highlight was getting to publish a book about my PhD research
Where I grew up, the schools were pretty bad and very few people went to university. I was the first person from my school ever to go to Oxford or Cambridge, and in the end I went to both - so I've learnt to never let other people tell me I can't do something!
At university I studied archaeology, and did my PhD on the archaeology of human evolution in South Africa, Lesotho, India and Sri Lanka. I got to travel all around the world for excavations, including three months in Madagascar. But after working on the analysis of stone tools, I realised I wanted to concentrate on data analysis and my technical skills. I joined the Civil Service Fast stream as a statistician, and worked in the Department of Health and the Treasury. Then I decided to move into startups, because that kind of fast-paced environment really suits me. I worked in a property technology startup, and then decided to do a data science bootcamp to improve my technical skills. Then I worked as a data scientist at a travel app company called Citymapper (you may have heard of it!), and now I've moved to Amazon as a data scientist in Prime Video. I still love to travel in my spare time though!
Information technology IT / ICT
My main piece of advice is not to worry if you're not sure what you want to do for a job - most people aren't either! You're allowed to change your mind about your career as often as you want. I was an archaeologist, then a civil servant, and now a data scientist, and the journey has been so much fun. You can do whatever you want to do!
Research, data and analytics
BA Archaeology and Anthropology
University of Cambridge
Data Science bootcamp
University of Oxford
University of Oxford
English Literature, History, Mathematics, Fine art
Samuel Whitbread Upper School
I only took maths A level because I thought it would give me a bit of a break from all the essay-writing in history and English, but it turned out to be one of the best decisions I ever made. When I decided I wanted to go into a technical field and work in statistics and data analysis, I don't think I would have been able to get my first job as a statistician if I hadn't done maths at A level. So I'd definitely recommend it to anyone who hasn't decided yet what subjects to take, as you never know when it will come in handy!
Data science, machine learning and statistics - technical skills are the most important part of my job. The languages I use are Python and SQL, but in the past I have also used R.
Research and analysis - general research skills are really useful, like the ability to read an article and be able to summarise what it says in a clear way.
Communication - communication skills are really useful in my day-to-day work, especially for communicating the results of my projects to the rest of my company.
Prioritisation - there's never quite enough time to get everything done, so figuring out what to prioritise and what you can leave for now is really important.
In order to improve my communication skills, I give talks and presentations whenever I get the opportunity, and I blog about things that interest me (mostly data science!) on Medium. This is a really great platform for practising your writing skills and getting your posts read by lots of people.
On the technical side of things, I did a five month data science bootcamp because I was impatient to learn the skills I needed to work in data science. But this definitely isn't necessary if you want to learn about data and analysis. There are lots of great (and often free) resources online. My favourite for statistics is Khan Academy: https://www.khanacademy.org/
I had no idea! I originally wanted to study English literature because it was my favourite subject at school, but I didn't really know what I wanted to do with it. Then I read about anthropology (the study of human behaviour and societies), and decided I wanted to be an anthropologist or an archaeologist. Then I was an archaeologist, but decided I didn't want to be one after all! Eventually I realised I wanted to work with data, and now I'm very happy as a data scientist. It's absolutely fine not to know what you want to do when you're at school, because the journey to figure it out is such a rewarding process!
I would have spent more time developing my technical skills earlier on, and learnt to code as soon as possible - it's such a useful skill and will always be in demand.