A trio of Evo Pricing data scientists take a break from their algorithms and charts to compile an overview of their daily working life. Tweet This
There isn’t really a typical day for a data scientist, but certainly there’s no argument about how to start the morning - with a rich breakfast and a big cup of coffee.
Then the fun part of the day can begin :-)
You sit down and focus on a particular problem, but it’s rarely the same problem from one week to the next. For each problem there can be new questions that require different approaches. And there’s the possibility that these are questions that are not just new to you, but new to the world, in their own little way.
This is both the challenge and the excitement of data science. Uncertainty isn’t just a statistical property, it’s a way of life.
Translating a business problem into a rigorous research project, and then translating the results back into a practical solution requires a deep understanding of the business domain and no small amount of creativity. Indeed, a data science team will never be successful working in seclusion; keeping a close relationship with the people out in the field and with their everyday challenges is the only way to bridge the gap between data itself and what it can tell us about the world.
At the end of the day, we will reflect on what we’ve learned and take that new bit of knowledge about the world into tomorrow’s work. But we are not done when the code compiles - we have to take what we have learned and effectively communicate it to multiple audiences, including the senior managers of our clients.
Fundamentally, we need to tell a story with the data, and since there is no right way to construct a data-driven story, we look for ways to visualize our results in intuitive charts, or assemble a presentation deck that walks through the problem to its solution, or even just say: “OK, here’s the answer”.
The point is, once we’re done, everyone should be able to understand what we did, and why - therefore, what the emerging insights are, and crucially, what this means for our clients in terms of decisions and expected results.
Data Science isn’t a job description, it’s a process. It’s true, you need all those skills, and more, to successfully carry out a data science project. But don’t get hung up on thinking you need them all rolled into a single person: Data science is a team effort.
A great data science team must span across the whole skill-set described above, bringing together a mixture of technical specialists and communication generalists, client-facing team and core back-end developers. But they must all, no matter their background, share a fundamental passion for intellectual curiosity. For if there’s one thing that truly defines the data scientist, it is the continuous learning mode.
The 3 data scientists who contributed to this article:
Elena Pesce worked as a Junior Data Scientist at Evo Pricing. She graduated cum-laude her Master’s degree in Stochastics and Data Science at the University of Turin, while she entertains audiences with long, complex and elaborate jokes (very often at the expense of her audience!).
Elena Marocco graduated cum-laude in Mathematics from the University of Turin and today is a Senior Data Scientist at Evo Pricing. She is famous for having explained her first work project to a celebrity on national radio, and also at a scientific conference in English, all in her first year of work.
Viola Pettinati is one of Evo Pricing’s latest hires; she has a Master’s degree in Theoretical Mathematics from Università Cattolica del Sacro Cuore in Brescia and a PhD in Mathematical Models and Methods in Engineering from the Politecnico di Milano.
About the author
Martin Luxton is a writer and content strategist who specializes in explaining how technology affects business and everyday life.
Big Data and Predictive Analytics are here to stay and we have only just begun tapping into their enormous potential.