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Women in STEM: Itoro Liney, Principality Building Society

In the second installment of our Women in STEM series, we spoke to Itoro Liney, Data Science and Analytics Manager at Principality Building Society.

Itoro gave us an insight into her career journey so far, how data science is influencing the financial services sector and her predictions on future industry trends.

Tell us about your career and what’s led you to a role in data.

After completing a Bachelor of Engineering at Cardiff University, I worked as a micro-electronic engineer at what is now called Newport Wafer Fab. My role as an engineer involved analysing huge amounts of test data from electronic chips in order to detect anomalies and understand faults. I then started a master’s programme in Operational Research, Applied Statistics and Risk which gave me the theoretical grounding to a lot of the statistical analysis I was carrying out as an engineer. The master’s programme exposed me to other industries which I found fascinating, especially financial services and I decided it was time to make a career change. I’ve spent the last few years in the financial services industry, applying statistical analysis to data in order to help organisations make data driven decisions.

More recently, I’ve started a PhD in computer science at King’s College London. My research centres on the role machine learning plays in perpetuating ethical bias. I’m hoping my research will contribute towards making financial service organisations aware of the dangers of bias and how to avoid it when we carry out complex analysis.

In summary, my entire career has always had a strong data element to it.

Did you face any barriers when building your career in data?

Not specifically. As I mentioned previously, my entire career has always been around data. However, one challenge I faced was during my career transition from engineering to financial services. It was difficult to understand how to apply my technical knowledge in ways that would be relevant to my stakeholders in a new industry. Sometimes, industry knowledge, subject matter expertise and context is a bigger barrier than technical data skills.

How has the industry changed in recent years?

The industry has changed a lot in the sense that organisations are now capturing more data than ever before. A lot of business leaders have realised that it is critical to harness the insights captured in their data and this has changed the landscape for data and analytics. New technologies are being used to process and store data. Techniques to mine structured and unstructured data have become more common. Tools and processes to enable the business-users consume data in real-time and user-friendly ways have become more prevalent. Data science functions have become more established and different roles have emerged to meet the growing demand of data. This has led to an increase in academic programmes and courses to support this growing demand. Overall, it’s an exciting time to be a part of this industry as the change is not slowing down.

What would you say are the main attributes required for someone to work in data?

I would say the main attributes to work in data are: having the ability to communicate with non-technical stakeholders – whatever analysis we do, it is important to remember, that it is aimed at the business user, who may not have a background in statistics or computer science;

Being patient – wrangling data and carrying out analysis takes time and is fraught with challenges;

Being courageous – sometimes, we need to tell stakeholders that the data doesn’t support their claim; and most importantly, great problem solving skills i.e. the ability to focus on solving the problem not the tools or techniques.

Do you have any predictions around future industry trends?

Big data will continue to be a key part of the data landscape. More and more organisations will look to migrate to cloud and will look to gather insights from various types of data.

The uptake of machine learning will continue to rise and a lot of organisations will look to deploy machine learning solutions to more problems.

Machine Learning / AI ethical concerns will become a bigger consideration for organisations to ensure that their models are not discriminatory. Based on this, there will be a move from model-centric AI towards data-centric AI i.e. we will focus on finding the best data for our problem as opposed to finding the best algorithm. Data scientists and leaders will have to be more ethically minded.

What is the most rewarding part of a career in tech/data?

There are lots of rewarding elements to my career. I love bringing objectivity to a conversation and solving problems using data, it’s like being a data detective. My research is also very rewarding, because anything that advances the data and analytics landscape is great. But perhaps the most rewarding part of my career in data/tech is mentoring. There is an influx of inexperienced data scientists who need coaching and mentoring, it is an important role to help build knowledge for the next wave data scientists.

What advice would you give to other females looking to start a career in data?

My biggest advice would be to find a good mentor. They don’t have to be female but they should understand your challenges and aspirations. When I was an engineer, there were no female engineering managers or senior leaders in my organisation so it was difficult to be inspired. Luckily, the financial service industry is a lot more diverse and I’ve always had a female mentor. This has benefited my career tremendously.

Thanks to Itoro for taking part.

We’ll be launching the recruitment phase of our 2022 Fast Track Data/AI Graduate Programme very soon, watch this space!