Below is a revised version of a short talk I gave at an event for women and non-binary people in tech. I chose to talk about why we shouldn’t let our job titles define our interests.
A few years ago, I was working for a startup as a data scientist and I was very unhappy. My manager at the time even told me over lunch one day that I was a mediocre data scientist and that he didn’t know what to do about it. Let me just let that sink in a for a minute: a mediocre data scientist.
Now, I could spend the rest of this post ranting about what a hard time I had with that manager but I don’t think that would make for an enjoyable read. Let’s just say that, harsh words aside, there was some truth to what he told me that day. I wasn’t excited about my work and it showed.
Since that day, I’m pleased and relieved to say that my role has changed. Right now, I would describe my role as 50% data scientist, 50% agile coach.
Back then, I was 100% data scientist but somehow I could never bring myself to say the sentence ‘I am a data scientist’ without my voice suddenly becoming more hesitant or using air quotes around the words ‘data scientist’. I think my weird reaction was partly due to the vagueness of the role — it’s only in the last couple years that ‘data scientist’ has become a widely accepted job title — and partly due to the fact that I didn’t feel like I fit that (vague) role.
So I asked myself, how could I fit the role better?
Approach 1: Get really good at one area
My first approach was to focus on getting really good at one area, preferably an area I enjoyed. I decided to focus on data visualisation. I ordered some books, took an online course and joined an online community of data visualisation enthusiasts (shout-out to the wonderful #MakeoverMonday community!). Eventually though the interest fizzled. Perhaps because there wasn’t much interest in the topic at work, perhaps because I didn’t have a mentor or anyone I could work with on data visualisation projects.
Approach 2: Get good enough at the hottest areas
After my initial approach failed to make me feel like a ‘whole’ data scientist, I decided to change my tactic and focus on mastering the skills that seemed to be the most in demand, skills like deep learning. I tried to ignore the fact that I wasn’t excited about the topic — I can’t remember if I told myself to just ‘suck it up’ or if I believed I would get more into it the more I learned about it. Whatever it was, giving up was not an option.
Meanwhile, time passed and I was now working at a new company. I had made some progress with deep learning — I was fairly comfortable using Google’s deep learning library TensorFlow — but I still felt like I had only scratched the surface. I told my new manager that I thought it would be good for me to improve my deep learning skills so I signed up to an online deep learning certification program. It was made out of 4 or 5 courses and would take me me about 3 months to complete if I took all the courses back-to-back. Yes, it would require dedication, but it was winter in Berlin so I had plenty of dark and cold evenings and weekends ahead of me.
I lasted about 2 months.
It wasn’t the content of the course that bothered me, it was the fact that the whole thing felt like a chore. I found myself doing the homework just to get the credit I needed to pass the course. My long study hours were also taking a toll on my relationship with my boyfriend as I didn’t have much free time anymore. I decided to give myself a break and take a couple weeks off in between courses. By the end of those 2 weeks my decision was made: no more deep learning. Luckily, the news didn’t seem to bother my manager that much. And it wasn’t like I was turning my back on data science as a whole. There were still areas I enjoyed, like data analysis and data visualisation.
OK, but where does the agile coaching come in?
Approach 3: Listen to your interests
As I was finally coming to terms with the fact that perhaps ‘data scientist’ wasn’t what I wanted to be called anymore, a colleague from another team started running retrospectives for our team. I found the format really interesting so with my manager’s support, I asked him if I could help him run one.
We planned the upcoming retrospective together and I facilitated the first half, my colleague the second half. It didn’t go perfectly but it gave me an idea of the process. It also gave me the confidence I needed to start putting myself forward for other activities that had nothing to do with data science.
Shortly after that experience the coach left. Suddenly, there were gaps across the company that needed to be filled so I volunteered to fill some of them. I told my manager I wanted to get more involved in coaching, I told colleagues that I was interested in coaching, my manager started telling people I was available for coaching. Soon enough, people started coming to me and asking if I could help out, and I have yet to say no. It hasn’t been easy, there’s so much I have to learn. But having opportunities and support at work to practice my coaching skills has given me a tremendous push to learn and improve my skills outside of work too.
The point I’d like to make is don’t let a job title define your interests. As tech workers, we don’t have many role models but we do have a lot of freedom and flexibility. If you want to try something, ask if you can help and tell people you’re excited about the topic. Before you know it, they’ll start coming to you asking for your input.
In my case, it started with asking my colleague if I could help run a retrospective with him.
