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 Chris:

I’m a Data Science Consultant with a passion for using data to make a difference. This has developed over the past 25 years whilst working as an engineering manager, product manager, data scientist and CTO. Throughout all the areas I have worked – edtech, ecommerce, market research, climate and security – I have had a desire to combine my technical and business skills to enable organisations to take advantage of their data to make a meaningful impact.

This led me to recently set up my own consultancy, Justified AI, where I help clients to use Natural Language Processing, Large Language Models (LLMs) and Machine Learning to solve challenging business problems. Before starting my own business, I was Lead Machine Learning Engineer for Tessian where I worked on the detection of malicious phishing emails using LLMs. Prior to this I was CTO for a climate tech startup, where I developed a semantic search system for climate policy and legislation. In a different field, I was responsible for Applied Machine Learning Research at NielsenIQ, primarily working on the large scale attribution of retail and ecommerce product data.

How did you start out in your tech career?

I started my technology career with Tribal, an education technology provider, with responsibility for local government and education information systems and implementation of national government technology initiatives. Through this work, I developed a passion for working with clients to help them gain insight from large and complex data sources. This saw me work as a Business Intelligence Consultant, building data warehouse and analytics solutions for local government and education clients. I also led the research and technical development of an interactive analytics product for schools.

I developed my interest in machine learning in 2011 as part of Tribal’s innovation group. I was responsible for the research and development a new student analytics platform for higher education which used machine learning to help identify students who are struggling and benefit from additional support. Whilst doing this, I decided to study for a part time data science masters degree, building on the skills I learned in my undergraduate degree and in industry.

Over the past 12 years, I have specialised in machine learning and natural language processing, by working as a Data Scientist and leading data science teams for organisations working with large amounts of unstructured textual data. This has seen me work on a variety of really interesting projects and with amazing datasets, where I have been able to apply and hone my skills and experience over time.

What are the signs of success in your field?

I’ve seen too many data science projects fail because they weren’t solving the right problem. Partnering with the business and spending time with them to understand the problem they need to tackle and, importantly, understanding why, will set you up on a firm footing to deliver successful data science projects.

What is the best and worst thing about your job role?

One of the things I love the most about working in Data Science is the opportunity to learn something new and apply it to my everyday work. The field is moving very quickly and it is important to stay up to date with new techniques and tools. I love learning and the great thing about working in Data Science is that the research and industry community is so collaborative and willing to share their experience and expertise! I set aside time to try to read about something new each day. This keeps me fresh!

The worst thing is also one of my faves 😉 Working in Data Science, I love the fact that we get to work on interesting and intellectually challenging problems. The downside of this is that it can be hard to put them down at the end of the day. I often find myself thinking about problems and possible new projects outside of the working day

What can you advise someone just starting out to be successful?

When I started out in tech, Data Science didn’t exist as a term and there were certainly no Data Scientists (although they might have been called Statistician’s back then). What you might be doing in 20 years time probably hasn’t been conceived of yet. Spend time trying out different things and find the things you love working on and are passionate about. This takes some time – don’t try and rush it. Although you might not be in your perfect role yet, you’ll learn valuable skills in all the roles you do that will be useful later on. Looking back, the skills I have learned and experience I have gained across all my roles have proved to be invaluable as a Data Scientist.

How do you switch off?

I’m fortunate to live somewhere with easy access to the countryside, so I love nothing more than jumping on my bike and going for a ride (or using my bike trainer if its cold and wet outside, which it often is in the UK!), going for a run, or walking my dog Maddie over the fields.

When I’ve got a bit more time, I enjoy brewing beer, and, of course, sampling the results!

Recently, I’ve started building a drone, but have realised that my soldering skills need a bit of work!

What advice would you give your younger self?

When I was younger, the thing I was most interested in was the technology. I love solving problems with technology. As I’ve got older, I’ve realised that one of the most important aspects of doing that is communication and, particularly, understanding the context. Dedicate time to understanding the industry in which you find yourself – its terminology, processes and people – the things that make it unique. Spend time with industry and subject matter experts. By doing this, you will build better technical solutions which solve meaningful and, importantly, the right problems.

What is next for you?

This year, I’d like to get more involved with some data for good projects.

If you could do another job now, what would you do? Why?

Run a bakery making home made scones. I’m a bit of a scone fan – my friends call me the Sconnoisseur 😉

What are your top 5 predictions in tech for the next 5 years?

  1. In the short term, the development of Small Language Models with similar capabilities as models many times their size. This will open up the potential for natural language solutions which are currently only available to the largest companies due to their cost.
  2. Multimodal Generative AI that can leverage images, text and video will be used by businesses both large and small to automate processes and create new solutions to problems that are currently challenging to solve.
  3. Legislation for the ethical use of AI will be put in place to help ensure that AI is used for the right reasons and that its impact on society is managed.
  4. Techniques will improve for detecting and countering bias when training AI models to ensure that models do not reinforce bias present in their training data.
  5. Outside of AI, improvements in battery technology will make it cheaper to store energy created from renewable sources to provide electricity at the times when renewables aren’t generating.

Watch Chris’ 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!