Ai in data science job applications
I’ve talked a couple of times about people using AI to apply for jobs in my team. I don’t entirely feel like I’ve got my point across so I thought I’d write a blog post about it. I think sometimes people think that I actively want to weed out people who need AI to help them write an application form. So I’d like to say upfront that I do not want to weed out that kind of person.
Valuing thought
I think I already learned the lesson that is the subject of this blog post before but I’m having to relearn it in my new role as a data science manager rather than a data science do-er. When I first started to get into data science I made the mistake I think a lot of people make where they think “I’m doing data science, data science is typing code, I want to do lots of data science, let’s type a lot of code”.
Application form advice
OK, so I’ve finished interviewing lots of data scientists over the last few weeks. I’m sorry that there were so many applications for the data scientist (hundreds) that I really can’t give people feedback to individuals whose application was not selected for interview. However, I thought what I could do is give some advice for people who were not successful in getting to interview, and quite honestly I would say the same to a lot of you anyway so I think it’s almost as good.
Job Questions
I’m recruiting at the moment. I’ve had quite a lot of people ask questions about the job and I thought rather than answer some questions for some people I would just answer all the questions on my blog and then point people here. Before I do I will make a couple of observations about the recruitment process. Everything is done from the application form which is stripped of identifying information. We don’t look at CVs and I don’t take into account any previous correspondence with individuals when we’re marking the application form.
Strategy
Another one in my series of “Wot I reckon” posts based on no science or evidence at all, just me trying to figure stuff out. So proceed at your own risk. This post is a bit more dangerous than the previous posts I wrote (for me, I mean) because it may get a little bit ranty in the middle (which I will try to avoid) but also because I don’t think it really paints me in the best light, or certainly the old me it doesn’t paint in a good light.
Decisions Decisions
Part two in my “Wot I did next” series of blogs about management/ leadership in data science, and the usual caveats apply- I claim no expertise in any of the subjects therein. I’m doing and learning stuff at the moment, and throughout most of my career I’ve written them down on the internet for my own reference and in case they help others. This one is, like the last one, about something that made me more uncomfortable the further I got into it.
New Job New Blog
This blog has existed for a long time. The first entry is nearly thirteen years old. I’ve always been a big believer in blogging and I have met many people and learned many things from blogging. It kind of tailed off a bit as I got busier and I haven’t really blogged at all since I got my new job in January 2023 (just one entry since then). I used to blog technical stuff, bits of code and whatnot.
Data science panel questions
I was on a panel the other day talking about data science careers (I guess there will be a link to it somewhere at some point, I’ll share it when I see it) and they’ve sent some questions out after the event because they didn’t get through them all. I may as well answer them here where everyone can see them rather than just have the people who came read them (hmm… might be a general point about working in the open there somewhere 🤔😉).
RAP; a 10 year journey
I found the earliest possible reference that I ever made to RAP in a document, part of an evaluation report I submitted nearly 10 years ago. At the time I wrote it I had hoped that it would be ceased on by the people who read it as an obviously important innovation, but I can see now that change is not so easily won. Anyway, just for fun, here it is, June 25th 2012, written in Sweave before all that newfangled RMarkdown (even that’s not newfangled now!
Data science for dummies (Goldacre)
Building your own tools for data science is a pretty fundamental concept, and I think it’s fair to say that it’s totally alien to most NHS bosses. I shall henceforth be showing them this excellent section from the Goldacre report. It’s not about data science as such, it’s about TREs, but it illustrates the concept beautifully The data science team at the music-streaming service Spotify do innovative work with data that helps drive the usability and popularity of their subscription service.