Cara Baestlein is a Data Scientist at London-based Snowplow Analytics. Before getting her MSc in Economics from University College London, Cara received her undergraduate degree from Edinburgh University, where she realized her interest in Data Science while spending a year abroad at Columbia University.
After university, Cara worked in finance, consulting, and startups, gaining the experience she now brings to her work helping companies of all sizes design and implement the data collection they need to build data-driven cultures at their organization.
I studied economics at university, first in Edinburgh and then at UCL. During an exchange year at Columbia University I took an introductory course in Computer Science, and that really awakened my interest in applying all the statistical techniques I was learning in Econometrics classes to real world data. For my Masters thesis at UCL I applied Vector Autoregressive models to estimate the effect of Quantitative Easing during the recent financial crisis. And after graduating, I participated in research project using machine learning to determine the sentiment in the German population towards refugees based on Twitter feeds. What I enjoyed most was putting the economic theories I was learning to the test.
But while in the economic setting you mostly have to rely on data sets prepared by others, with large lags and imperfect assumptions, in the digital analytics world data can easily be collected in real time. As part of Snowplow’s Professional Services team, I get to help clients around the world not only design and implement what data they want to collect, but also to use the data most effectively to answer the questions that make the difference to their business.
Find out what you are really passionate about, what you would be happy spending every day doing, as opposed to a job that you like the idea of more than the actual day-to-day work. And keep an open mind about what your ideal job might look like, whether it be what industry you will work in, what location, or what size of company. You might surprise yourself and end up enjoying a job you never set out to get.
A real surprise to me was that business culture seems to be a bigger blocker in using data effectively than a lack of sophisticated tools to do so. The technologies available today really enable even the smallest of companies to use data effectively to drive their decision making. Yet a lack of understanding and trust in these technologies often means they are not adopted as widely as you would expect.
“Especially at the beginning of a career I think it is really important to feel like you are growing through your work. It can be scary always reaching slightly further than what you feel comfortable with, but in my experience that simply is the fastest way to learn.”
Technology is evolving rapidly, and while this provides wonderful new opportunities for growth, it also presents new challenges. Leadership needs keep up with the progress of technology, adapting and responding to new ways of working and doing business to continually be able to attract happy customers and highly skilled and enthusiastic employees.
I think it can be tremendously valuable to continually identify new areas you are curious about and invest some time into learning new skills. Especially at the beginning of a career I think it is really important to feel like you are growing through your work. It can be scary always reaching slightly further than what you feel comfortable with, but in my experience that simply is the fastest way to learn.
Definitely, I can think of two reasons in particular: firstly, data allows companies to evaluate strategies and performance more objectively, free from human biases or stereotypes. Secondly, because the field of data analytics is a relatively new industry, particularly with regards to digital data, the expected persona and career path of a great data scientist haven’t been solidified yet. And so it’s really more about your passion and aptitude for the job as opposed what university you went to or where you worked before.
I see data and the insights derived from it as another tool under our belt to help us make better decisions. It allows us to understand the problems we face agnostically, and tackle them more efficiently. I feel like it brings this whole new dimension to the way we work today: we can test and evaluate our strategies, practices and assumptions in a way that was previously only accessible to scientists, running experiments in labs. We can now run experiments in the real world, whether its A/B testing new website features or evaluating the performance of an algorithm recommending products to customers in real time.