Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Leigh:
Leigh Collier is a Quantitative Analyst and Data Scientist turned Start Up Founder with a passion for making data science less dry and fixing the major data science issues that we’ve just accepted as a fact of life. Outside of her technical career, she’s also a Leadership Coach for people who are Neurodivergent, Musician, Martial Artist and rescues Guinea Pigs.
How did you start out in your tech career?
If we’re talking about my first job then my first job title was literally a Bear Builder at Build A Bear Workshop. I did that for just over a year and came back once or twice in my breaks from uni.
Then my school was contacted by a social enterprise called FutureReach who were working on connecting students in years 12 and 13 with early internship opportunities. My head of sixth form suggested that I apply and after a few interviews I secured an internship at Bank of America.
It was there that I learned the power of networking. Before leaving that internship I asked if I could come back again. And I kept asking that question until I ended up with a full time offer. It was after my first year of University that my manager across the internships so far sat me down and got me to think intentionally about my career. When I did the research and identified that I wanted to become a quant, he set me up with people to talk to and I ended up doing two summer internships as a Quantitative Analyst before joining full time after graduating from my Masters Degree.
What are the signs of success in your field?
Honestly, it’s the soft skills that I think are more important than anything else. I was very good at building relationships with the people I worked with. I distinctly remember one of the senior guys on my team complaining that he’d emailed a senior on our tech team to ask a question but she hadn’t replied. The two of them hadn’t had the best relationship. So I sent her a nice email that basically asked the exact same question and got a reply back the same day.
The actual email I wrote wasn’t what made the difference, it was the relationship I’d built already. And I became very good at moving things forward because people were generally very willing to work with me because of how I conducted myself.
The other things that served me well were having a willingness to understand the problems my business partners were facing. I always say the most important skill in data science is being able to understand the business problem so that you can translate it to the mathematical problem. It’s definitely a skill that took a long time to learn and I have to credit the amazing mentors I had along the way. But again, that’s the building relationship part in action!
What is the best and worst thing about your job role?
The best part… I love a bit of problem solving. I’m a mathematician by education so puzzling out the solutions to problems is always a huge win.
The thing I actually struggled with the most in the actual quant/data science role was actually sitting around waiting for code to run. Years later I found out that I have ADHD, which I’m very open about and explains why I was so confused that others didn’t seem to be bothered by this waiting around. But yes, needing to rerun your model because the kernel died when you already waited ages for it because it’s big data and slow to run. That’s a special kind of pain for me.
What can you advise someone just starting out to be successful?
Learn stuff! Learning doesn’t stop when you finish education. Learn the WHY behind the things you’re doing too. Some people I worked with would write unit tests because they couldn’t release their code to production without them. They were just doing it to tick a box. Others of us understood that unit tests were to proactively stop code from breaking and degrading in production. Guess which group ended up spending the most time doing support?
The more you learn, the better you will be at your job and the more opportunities you will get. Be curious, ask questions and learn about areas outside of your own too.
How do you switch off?
Oh gosh. See aforementioned ADHD diagnosis. This is a real struggle for me! Personally, my brain is always spinning so I need things that completely absorb me. I’m a classical musician and regularly play in orchestras or solo piano concerts. I also do Brazilian Jiu Jitsu. It’s quite hard to think about work when you have a higher priority like not letting someone armbar you.
What advice would you give your younger self?
Learn what “good enough” is. Not everything requires or deserves your maximum effort. Save some to spend on the stuff that means something to you.
What is next for you?
As one of the two founders of Evil Works, I’m really excited about growing our Data Science Platform and getting more and more people to use it! It’s all about improving the data science experience and would have been a game changer for me back in my corp career.
One of our fundamental features is incremental computing, which is the concept that if the code has run successfully once and none of the code, inputs or data have changed, then don’t rerun it, grab it from the cache. This happens at every line of code on our PUFF platform which not only significantly speeds things up but also enables things like going backwards when debugging, undoing your recent change and getting the old result back instantly and recovering from kernel crashes immediately!
Personally, it’s more music, more BJJ, more spending time on the hobbies I love and with the people I love.
If you could do anything now, career-wise or personally what would you do? Why?
I really loved speaking at the Data Science Festival so I would love to do more of that. I think Data Science is often quite dry as a topic so I always enjoy ways to make it more interesting. If I can get a laugh out of the audience on a data science topic then I’m doing something right. So if you’d like to book me to come and speak, let me know!
What are your top 5 predictions in tech for the next 5 years?
- I think genAI will stabilise into something normal. Right now a lot of people are making huge predictions and things are moving quickly. I think a lot of the hype is overblown but also that there’s a lot of genuine use. I feel like we’ve been through the biggest disruption and are now in an adjustment period where we work out what it’s actually good at and where it actually fits. But I may well be wrong about this one!
- The ability to understand problems and think strategically will be even more important. Like I keep harping on, understanding your business problem is the most important data science skill. If vibe coding takes over as the main way of developing, well that’s okay but only if you can make sure it’s solving the right problem.
- Technical debt will balloon. It’s a side effect of the first two points really. Vibe coding creates tech debt and tech debt is dangerous. What I think is interesting here is some of the jobs that this will open up. For example, a lot of old banking systems were written in Cobol and Fortran and very few people are learning how to code in these languages anymore. Which means if you’re an expert in one of these languages, you can get paid a premium to sort things out when they go wrong. I think very good programmers will still be in demand because of the ability to sort out this tech debt with skills that can only be gained from experience.
- The decline of social media. A lot of the big tech companies out there are social media companies. Facebook, Twitter, TikTok. I don’t see the social media backlash dying down so it will be interesting to see the changes made in this space and what springs up in its place.
- Evil Works will become the lead data science platform based on its efficiency, ease of use and guardrails for both human and LLM developers alike.
I may be wrong about many of these but I hope I’m right about the last one!
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!