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Top Tips for Nurturing Your Data Scientist Talent
By Jiahang Zhong, Head of Data Science, Zopa
Who are the data scientists?
Although the job title of ‘data scientist’ has become extremely popular over the last few years, the nature of the work and required skill sets can be surprisingly different between organisations. At Zopa, our data scientists are primarily focused on developing machine learning and optimisation algorithms deployed rapidly in production to ensure a strong impact on customer experience and business outcomes. We found the following three groups of skill sets crucial for their success:
1. Statistical analytics to design experiments and data collection strategy
2. In-depth understanding of various machine learnings algorithms and their pros/cons
3. Knowledge in programming and software engineering to ensure that models can go smoothly to production
As the technologies and broad range of business applications continue to grow across all industries, it is actually very difficult for a data scientist to master all these skills.
In an environment that encourages curiosity and knowledge sharing, our data scientists can grow fast by learning from each other and more likely to innovate through collaboration
Instead of looking for a so-called “unicorn data scientist”, we found it more effective to build a team that complements each other’s skill sets. In an environment that encourages curiosity and knowledge sharing, our data scientists can grow fast by learning from each other and more likely to innovate through collaboration.
How do we work with other parts of the business?
In recent years, Zopa has largely benefited from a cross-functional organisation model which distributes individual skill sets and talents around each product into different working groups. We call these groups ‘tribes’. Each product tribe is able to make autonomous decisions in an agile fashion, without much dependency on other teams. Our data scientists are also embedded in these tribes with comprehensive ownership on all data and modelling related topics. Additionally, they get in-depth understanding of the product roadmap and key challenges throughout the business. This enables them to propose new ideas and make an impact much faster than froman isolated functional team.
This organisation structure relies on two key elements to succeed. First, the data scientist has to have good business acumen. More than just finding answers from their technical perspective, they must be able to ask the right question in the first place, with the broader picture of the business and product. It is also essential to have great communication skills to discuss issues and resolutions with the team and convince business stakeholders on their ideas.
Secondly, we put a huge emphasis on personal growth and development at Zopa, and this of course extends to our data science community. We ensure that each data scientist is managed by someone with expertise in data science, this helps to keep them engaged, be understood and helps discussions around any issues that may arise during projects. As the head of data science, I’m very passionate about nurturing and growing the talented individuals that we employ, plus ensuing that we create an environment in which people feel they can do their best work and a place they really want to be in.
Machine learning has always been a major part of our operation from the very start and will continue to be at the core of the business. Our recruiting and nurturing strategy has helped Zopa grow into a business that has evolved tremendously from its origins back in 2005, and we are proud that our dynamic culture and evolving mindset have remained within the company and continue to bring in talented individuals to work with.
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