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!
About Adam
Adam is a Lead Data Scientist at dunnhumby, where he leads the development of personalised recommender systems for multiple retailers across the globe. He works closely with engineering teams to improve MLOps within the business. Adam recently graduated from his Cognitive Science PhD at UCL. He publishes research combining big data, machine learning and ideas from psychology to better understand consumer behaviour.
How did you start out in your tech career?
By accident! During my undergraduate, I applied to every internship scheme that accepted Psychology students; most were HR roles in big banks. Thankfully, dunnhumby were the only company to make me an offer, for a Data Analyst position. I realised that lots of the programming and statistics that I’d learnt in my degree was very applicable to data analytics and customer behaviour. I loved it.
I heard about Data Scientists soon after joining dunnhumby, shortly after DJ Patil described it as the “sexiest job of the 21st century”. I became a bit obsessed, and spent my evening and weekends learning about machine learning and participating in Kaggle competitions. I was then lucky to score a full-time Data Scientist position within dunnhumby, working for a large retail bank.
What are the signs of success in your field?
Successful data scientists learn quickly and are good at translating complex vague ideas into clear, testable hypotheses. If I see a Data Scientist doing this, then I consider them successful. I could care less about academic qualifications or Kaggle rank. Data Science evolves quickly, so I think it’s also important for us to be listening, learning and having opinions about changes in the industry.
What is the best and worst thing about your job role?
The best thing is that I get to work with and learn from lots of clever people. I like borrowing ideas from engineers, project managers, product experts and the many other people I interact with. The worst thing is my long list of research projects; I wish I had time for them all!
What can you advise someone just starting out to be successful?
Pick one or two specialisms that help you to stand out. For example, if you’re interested in causality or recommender systems, read some papers and code up some relevant models when you have the time. Put them on GitHub. Specialisms are becoming increasingly valuable during hiring and it helps if you can have an informed opinion about some specialist topics during interviews.
How do you switch off?
Hanging out with family and friends. Music with good headphones also really helps.
What advice would you give your younger self?
I consider myself a “born again mathematician”. It’s been fun but also challenging learning mathematical concepts outside of school. I wish I’d spent more time learning basic maths when I was younger (e.g., linear algebra, probability & calculus).
What is next for you?
Adjusting to post-PhD life – I need to get some hobbies đ
If you could do anything now, what would you do? Why?
If I could choose any career now it would probably be an ML engineer. Data and machine learning models are only getting larger, so we need people to understand how best to scale everything up!
What are your top 5 predictions in tech for the next 5 years?
I’ll give one prediction – large language models such as GPT are going to be everywhere. I expect them to be embedded in nearly all software. The good news is that information search and retrieval is going to get much easier. The bad news is that we are going to become inundated with model-generated text; good search engines will focus primarily on understanding when (if ever) auto-generated content should be retrieved.
Watch Adamâs session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!