Charlotte Werger, a recent Fellow on the latest ASI Data Science Fellowship in May 2016 reflects on her journey, transitioning into a new Data Science career by taking part in the Fellowship.

Tell us about your background, before you decided to move into Data Science?

My background is perhaps a bit non-traditional for a typical data scientist. I’ve studied Economics throughout my student life, but always had a big interest in Econometrics. After completing my PhD in Economics at the European University Institute in Florence, I found a job at a quantitative equity team in BlackRock, a large asset management company. It is there that my interest in data science really developed. My fellow researchers were doing these very interesting things with Machine Learning, Natural Language processing etc. Those things were all new to me, but I wanted to get in on the action. I therefore started to study the topics myself and my interest in data science grew more and more.

Why did you choose the ASI Fellowship?

When I felt I was done learning at BlackRock, I decided I wanted to take time off to focus entirely on data science for a while. This would also help me pivot my career more towards data science. I therefore started to look around for courses or programmes to do so. The ASI came on my radar through a good friend that had participated a year ago, and was so enthusiastic about it. What drew me specifically to the ASI is the selection of people in the fellowship and in the ASI organisation. All participants are incredibly talented and have really impressive academic backgrounds. I thought these are by far the best group of people I can spend time studying data science with. I believe the network you can build up through the ASI is incredibly valuable, and unlike anywhere else.

What was the best part of your project or experience on the ASI Data Science Programme?

The ASI fellowship really helped build my confidence in believing I can undertake any data science project I set my mind to.

That sounds perhaps a bit weird, so let me explain: through the ASI program I did not only learn more about data science in general, I have also been given the tools to take my learning and development further as I now know where to look for resources and materials.

I have also gained access to a group of really bright data scientists that are always willing to help, and I have gained hands-on experience completing an actual commercial data science project. My Fellowship project was with Hudson Recruitment where I used Data Science techniques to predict performance in sales and executive teams. All of these aspects of the Fellowship combined really helped me feel more confident in the field of data science, and help me tackle data science problems in my current job.

See Charlotte's Demo Day Presentation here:

What would be your advice to people looking to enter Data Science in Industry?

First of all, for all data scientists the following applies: know the basics and know them really well. In most data science jobs you will work with data and explore statistical properties every day. Your knowledge of statistics, data analysis and basic coding therefore needs to be up to scratch. In interviews you are expected to answer technical questions around these topics. Be prepared by refreshing the basics and don’t be put-off by those questions, it’s pretty common practice.

Secondly there are many different types of data scientists and the work they do day-to- day differs significantly. Be aware of this when you apply for jobs. For example, at the ASI we made the distinction between “data engineers” and “data scientists”. The former focusses on effective data infrastructures, handles big data and works with software like Hadoop and Spark. The latter focusses on for example data analysis and uses machine learning algorithms to draw conclusions out of the data. Understanding where your interests lie and strengths are is important in the job search. The types describe above would be two quite different practical jobs, but in the job market these are both often classified as “data scientist”.

That brings me to point three. Many companies nowadays are looking for “data scientists” but some of them don’t know exactly what it is, and what they are looking for. I saw job descriptions with exhaustive lists of skills that didn’t seem quite appropriate for the job they were advertising for. Be aware of that and make sure to be thorough in your own due diligence. Ask yourself: what is the actual job they want me to do day-to- day, and does it fit with what I look for in a job?

Lastly, it’s always a good idea to attend meet-ups and data science events. That way you keep up-to-date with the developments in the data science industry, and learn what different companies are working on. The ASI holds regular Meetups with internal and external speakers in data science and engineering. You can join the ASI Meetup group here, but there are also many others in and around London.

How was your overall experience on the Fellowship, and what have you been up to since completing the Fellowship?

My overall experience was great; I learned a lot from the project I worked on, got to know some awesome people and it has pushed forward my data science career. After ASI I landed a job with Man AHL, London’s largest quantitative hedge fund. They actually participated on an earlier cohort of the ASI fellowship. I now work as a researcher in the equities team, and bring in my data science knowledge to expand and improve our trading strategies.

Man AHL actually has an entire team dedicated to researching the application of machine learning in investment and trading, so I am surrounded by a group of really smart people from which I can learn a lot. At AHL we actually host
data science Meetups as well, under the group named “PyData London Meetup”, and any data science enthusiast is welcome!