Interview with a Data Scientst: Keith Bawden

Keith Bawden worked with me at Amazon, but not directly. Despite the fact we only had interactions over internal chat and a few emails he was a great influence on my thinking about Software Development and how to use statistics to solve problems in Industry. He has over 10 years experience in the Tech industry including working with Analytics folks and System Engineers at places like Amazon and Groupon. I consider him a fine example of what a business-focused technologist should be, and consider him a great guy at doing ‘tech leadership’ – which is something we all find hard to explain but we know it when we see it. I provide the interview with few edits.

Keith: I’m no data scientist nor am I an expert at anything in particular. But here are my answers.
1. What project have you worked on do you wish you could go back to, and do better?
Keith: There are none. Every project I have done I have found out something new. Going back and changing something may change what I have discovered. Not necessarily for the better. Warts and all I will keep my history as it is 🙂
2. What advice do you have to younger analytics professionals and in particular PhD students in the Sciences?
Keith: Be pragmatic where possible.
3. What do you wish you knew earlier about being a data scientist?
Keith: Still learning so I have no idea how to answer this.
4. How do you respond when you hear the phrase ‘big data’?
Keith: I try to understand what the speaker means when they say big data. The term is not so clear and therefore often needs clarification when used in conversation.
5. What is the most exciting thing about your field?
Keith: People.
6. How do you go about framing a data problem – in particular, how do you avoid spending too long, how do you manage expectations etc. How do you know what is good enough? 
Keith: Depends on the problem and the context. However, the definition of success should ideally be defined earlier rather than later. Then stop working and deliver what you have the instant you have hit your mark.
7. Do you feel ‘Data Science’ is a thing – or do you feel it is just some Engineering functions rebranded? Do you think we could do more of the hypothesis driven scientific enquiry?
Keith: Again it depends on the context and the usage. Most of the time, IMHO, it is an area (or subset) of engineering that has been re-branded. However, I have seen one team that are staffed by some pretty smart stats people doing experiments and using the scientific method in the work place. However, even with this team I’m not sure they do this kind of work all of the time.

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