What makes a great data scientist and data science team? (Podcast)
I was recently honoured to be a guest on The Data Pubcast - a podcast about making data accessible to everyone, hosted by the incredibly talented Nick Latocha and Andy Crossley.
In the episode we discuss:
- the spectrum of what "data science" means and how knowing that can help you create excellent data science teams [4:10]
- Maker vs Manager schedules: how to carve out uninterrupted time of deep work for your team [11:00]
- how Data Product Managers can protect the time of those doing "flow state" work [13:00]
- the 3 skill categories that every excellent "Machine Learning Engineer" needs [14:00]
- one of the hardest problems that successful data science teams need to overcome [17:00]
- Data Engineers: why all successful product data science teams need them [18:00]
- does data democratisation work? (Empowering stakeholders to do analysis themselves) [25:30]
- a profound approach to creating an excellent data science team that the rest of the business want to support [30:20]
- why "delivery" is the second part of being a great team, and what that involves - including software engineer training, mathematical theory, and why data scientists need to be great communicators who practice and deliver thoughtful feedback to each other [36:00]
- one of the biggest mistakes I made that had a collossal impact on me (and the solution) [38:00]
- a few organisations that stand out as leaders of data science at the moment [41:49]
- why data scientists are expected more and more to prove their worth (and why execs need to believe and support their work) [45:00]
I also answer the 3 questions they ask every guest:
- What would you tell yourself as a 20-year old to help your data science career? [48:00]
- What #1 training course would you recommend to someone just starting a data career? [51:11]
- If you could sit down with anyone - dead or alive - who would you want to meet? [54:00]
I really enjoyed speaking to Nick and Andy, and I hope you enjoy listening.