I chaired a breakfast meeting for Women in Data Science recently, and one of the topics for discussion was how to retain talent. While demand is outstripping supply and the market is going crazy, it’s enough of a minefield finding good people in the first place.
Add to this that even after you’ve made an offer to someone, recruiters will be contacting them regularly to try to tempt them away to other roles. It’s impossible to prevent this. I’m a big believer in not playing games with recruitment – I know what I can afford and won’t get into a bidding war. If I’m paying a fair salary and they go elsewhere for money, then they are more likely to jump when a recruiter calls regardless of how well you incentivise them. This isn’t a big company or small company thing, if you want to keep hold of your team after you’ve done the very hard job of hiring them then you need to understand what motivates them and either make sure that you continue to provide those needs or plan to be hiring again in the next 12-24 months. Continue reading Incentivising data scientists
Yesterday I had the great pleasure in being part of the global WiDS2017 event show casing women in all aspects of data science. The main conference was held at Stanford but over 75 locations world wide had rebroadcasts and local events, of which Reading was one. In addition to spending a great evening with some amazing women, I was asked to speak in the career panel on my experiences and overall journey. Continue reading WiDS2017: Women in Data Science Reading
I’m currently building a team for my new secret project and far more of my time than I’d like is spent with the recruitment process. However, every minute of that time is essential and we’re at a point where none of it can be handed off to an agency even if I wanted to1. So getting the recruitment process right is essential.
One of the basic principles of management in any industry is that if you set metrics for your team, they will adapt to maximise those results: set a minimum number of bugs to be resolved and you’ll find the easy ones get picked off, set an average number of features and you’ll find everything held together with string, set too many metrics to cover all bases and you’ll end up with none of them hit and a demoralised (or non-existent) team2. The same is true of recruitment – you will end up hiring people who pass whatever recruitment tasks you set, not necessarily the type of person the company needs. While this may appear obvious, think back to the last interview you were at, either as the interviewer or interviewee – how much relation did the process really have to the role?
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