If you want to become a data scientist, then there are various skills you need in your arsenal. Having good knowledge of programming, statistics and data visualisation – not to mention the importance of machine learning – are obvious. But sometimes, the most important aspect is finding a company which fits your needs.

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“Someone had to say it,” wrote Jonny Brooks-Bartlett, data scientist at Deliveroo in a Medium post in March on the difficulties those in the industry faced. Admitting he was ‘playing devil’s advocate’ somewhat, Brooks-Bartlett noted that often, expectation did not match reality with some companies.

“The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic report,” Brooks-Bartlett wrote. “In contrast, the company only wanted a chart that they could present in their board meeting each day. The company then gets frustrated because they don’t see value being driven quickly enough and all of this leads to the data scientist being unhappy in their role.” So how much of this is true, and how much is knowing when to play politics at the right time? AI News caught up with Antonio Fragoso (left), senior data scientist and technical leader at IT and software development firm Globant, about what makes candidates stand out, and on what can be expected.

Fragoso is speaking at the AI & Big Data Expo , in Santa Clara on November 28-29, around best practices for implementing a natural language processing project within an organisation. Find out more about how you can attend here.

Hi Antonio. What does your role as a data scientist involve – and what is your day to day routine (if you have one)?

A typical day for a data scientist would involve working in between business, developers and some other experts in fields like user experience, big data and cloud. We help product owners to set up objectives and directions to construct AI/data products for millions of users (they have the expertise on the core business and we provide the ideas to convert raw data into valuable assets through complex transformations and modelling techniques).

We work side to side with developers and technology experts to translate models into the creation and mature of scalable products.

What in your opinion are the key attributes required to make a data scientist? How important is the understanding of AI and machine learning and its associated languages compared with a few years ago?

Besides great imagination, storytelling and of course proficiency in complex applied statistical modelling techniques (machine learning, deep learning) and coding, I think a data scientist nowadays should be deeply involved and hands on working in big data and cloud technologies, which will allow them to be properly involved in the whole data cycle as building highly standard AI products.[…]

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