How to become a data scientist, and how to create a data science team
It is difficult to define data science these days: every company claims to be doing data science and everyone claims to be a data scientist. Practitioners are puzzled by their fuzzy job descriptions, and people who are trying to become data scientists are frustrated by the lack of standard definitions. In this conversation, at Toronto Machine Learning Summit 2017, we have tried to demystify data science and clarify what it means to be a data scientist.
Data science is applying scientific methods to understanding data in order to solve business problems. “A good data scientist in my mind is the person that takes the science part in data science very seriously; a person who is able to find problems and solve them using statistics, machine learning, and distributed computing.” said Amir Hajian, Director of Research at Thomson Reuters Labs. In other words, data scientists are people who can “reason through data using inferential reasoning, think in terms of probabilities, be scientific and systematic, and make data work at scale using software engineering best practices,” said Baiju Devani, Vice President of Analytics at Aviva. He added that It is important to recognize that, “there is no deterministic path to the problems you’re solving or the solutions you find, so you have to be ok with fuzziness,” He also states that you need to, “have that experimental mindset that allows you to work with vague problem and solution definitions.” In some sense, the best data scientists are people with “good statistical knowledge, programming and technical skills, and industry experience” according to Lindsay Farber, Senior Data Scientist at MoneyKey.
Ozge Yeloglu, Chief Data Scientist at Microsoft Canada also reminds us that in business there is a need “to step back and get educated about what data science really is. It is the use of data to address business problems which sometimes means there is no need to use machine learning or artificial intelligence.” Part of the misunderstandings that exists around data science originate from the hype that exists. Every business is in rush to get into data science without taking the time to understand why and how. Big companies are in a fierce competition to hire as many data scientists as they can without taking the foundational steps needed. “If you have 100-200 data scientists (compared to say Facebook who has 500-600 data scientists), you are in the wrong business”, said Baiju Devani. He added “while these organizations have big problems to solve through data science or machine learning, they have not thought about operationalization to bring those solutions at scale.” On the other hand, there are startups who are too eager to solve everything with machine learning without thinking about the proper scale of the problems. “If you are a startup with a thousand clients, you should not be doing sentiment analysis instead of calling and talking to every one of your clients,” said Baiju Devani. Practitioners are also rushing into becoming data scientists without understanding what it means. “Everyone who has taken a few online courses thinks they have transitioned without putting nearly enough time and effort into it,” said Ozge Yeloglu.
The hype is not completely unjustified, however. There has been quite a few interesting business use cases that have been creating an excitement in the industry. “I really feel happy when our clients come to us with a real data business problem and we can help them with our resources while leveraging their domain knowledge; that makes me optimistic that the hype can get us to the reality sooner than later,” said Ozge Yeloglu. “Organizations want to find this new way of of doing business in a new direction that was not possible before,” said Amir Hajian. He added that practitioners want to be data scientists because “they want jobs that do not end, and are not repeating the same thing over and over again every day.
