Women’s Day Special : Interview with Deborah Buckman : Co-Founder & Vice President- Decision Science @ Zinia.ai
Deborah is Vice President- Data Science at Zinia AI. She has 15+ Years in analytics /financial services and a BA (Hons): Business Studies – Business School at Kingston University, UK
Before Zinia, what did you do? Please give us your background?
· Prior to working at Zinia I worked for 2 companies; 5 years in insurance at one company and about 5 years in financial services at another, both undertaking analytics and predictive modelling. After, I worked as a consultant on project-based work for 3 to 4 years where delivery and working on end-to-end processes independently was expected.
How did the idea behind Zinia arise?
· Aashutosh (Co-Founder & CEO) reached out to me and we met in a coffee shop. He sounded out a decision module concept. We discussed how it could work in a mechanical sense, how it could be integrated and which process and technologies would be required to support it. I went away and thought further about how the concept could be brought to life. We met a couple of times over 5 or 6 months before I brought in a mini prototype that I had developed to demonstrate how Aashutosh and Randeep (Co-Founder & CAO) could build the first product iteration internally. I focused on coding the User Interface and making the AI code Randeep had passed me more dynamic for user inputs.
What was the biggest challenge of Zinia first year?
· I joined in 2019 to build the prototype that I was trying to convince Aashutosh and Randeep to build themselves. I knew we were strong analytically but building an AI tech application was something I hadn’t done previously, so I was out of my comfort zone. I was determined and thought, ‘just because I have not done it before it doesn’t mean I can’t do it’. We were resource constrained with no additional technical resource and no budget, so we had to produce something from nothing by ourselves. It was challenging and I had to do a lot of research in coding in order to produce a prototype we were happy to showcase. It was the most exciting stages looking back, we would review the development each week and it transformed quite quickly. I felt that I made a valued early contribution to the product and that was satisfying. Once we gained funding and severed the prototype user interface for our preferred cloud hosted alternative, we created tight deadlines to make the transition and that was the most difficult time. More new technology to conquer and restricted time to get familiar with it.
What motivated you to keep going in the challenging early days?
· I had internal drive to create and complete the beta and version 1.0 that I had promised. Working outside of ‘normal’ business hours was okay for me because I was dedicated and I have a young child so I worked late into the night, when I was not needed. So, it just worked out.
Do you have any advice for young girls getting into Data Science?
· I am all for sharing and acknowledging achievements, for me gender does not come into it.
My advice to anyone looking for a role in this area is to think about being good in all parts of the process from data extraction, data manipulation, modelling and summarising the findings. Also, think about articulating the key points to any audience and refrain from using too many in-depth technical terms to less technical audiences in the business as it only serves to creating a barrier to model adoption. We can get caught up in the smaller issues and problems to resolve and that stops us from seeing the bigger picture or business application, it is good to take a step back and ask, how important is this?
I think businesses can attract more people into analytical roles by offering flexible working hours, remote working and part time hours as a standard practice rather than an exception, this will make it a more attractive offering for anyone considering a career in this field.
For me, it is not about being a woman amongst men but more about delivering what I promised, to the best of my ability.