Source: Medium | 2019
Author: Maksim Butsenko
Data science is an integral part of building an efficient ride-hailing platform. At Taxify, it took us just one year to build a strong and agile data science function which works on state-of-the-art solutions and deals with optimising millions of rides happening in real time.
While interviewing hundreds of candidates, we’ve realised that even those with a strong technical background were very often lacking some essential skills. In this article, we’re talking about things that they don’t teach you in Machine Learning courses.
Defining the role of a data scientist
Tech industry has (more or less) learned how to make engineers and business work together. We have several product development approaches to choose from — it is up to your team to setup either Scrum, Kanban, or XP workflow. People know how to fit those flows into organisation and you’ll find a lot of advice out there on how to apply it efficiently. What often lacks, however, is the understanding of how data scientists should fit into this picture. Where to place them — engineering or business? Do you unleash them to look for the insights that nobody has? Or do you ask them to answer a very specific question and improve one narrow field in your business?
Asko Seeba explains very nicely the process of how business might look at data science project and argues that it is mainly a research project and should be considered as such. Considering that even people in the industry still try to understand how to utilise data scientist’s expertise in the most efficient way, how should new joiners know what skills they should focus on?
Building a team
A year ago we started building a top data science team at Taxify. Considering our growth (the number of rides on Taxify grew tenfold last year and we’re serving more than 15 million passengers and 500,000 drivers globally) and amount of data-related challenges in the transportation field, simply hiring a few great data scientists would not be enough. We are looking at adding a few dozen more data scientists to our team in the next year.
But who exactly are we looking for? The proper academic degree programs in data science are just emerging and the industry itself is not yet completely sure how to define a nicely framed profile of a data scientist.
As a matter of fact, the pool of data scientists currently consists of individuals with various backgrounds. There are people with background in computer science and AI in our team, but also those who come from the fields of signal processing, econometrics, chemistry, complex systems, sociology etc. Our common denominator is usually a good understanding of scientific method and of the design of experiments. Technical skills are much more straightforward to acquire. However, as we come from various fields, our understanding of processes around delivering data-based products might differ. It takes some effort to integrate all these experiences and deliver strong team results, and here’s how we handle this.