I am a seasoned global leader in analytics and data science with more than 20 years’ experience. I have a solid track record of combining a strong business sense with analytical expertise and results focus to deliver significant value.
I have demonstrated high adaptability throughout my career, having led several multi-cultural and multi-functional teams across the world, directly and remotely in various industries (financial services, banking, e-commerce), in high growth and mature environments alike.
In the past few years, I have regularly shared my insights at conferences in panels, presentations, roundtables or in advisory roles. My deep expertise covers a wide range of analytics applications, ranging from credit risk to marketing, customer and web analytics, cross-selling and pricing.
I started my career in academia, where I gained a strong theoretical foundation which, combined with a pragmatic approach, built on my exposure to real problems and data for many different applications through university consultancy.
I then developed my business skills during my first few years at Capital One in the US. Throughout my career, I have challenged myself by working for innovative companies across different industries. I am is also staying at the forefront on all innovations and development in the broader data world in order to be able to apply them to relevant industries.
To date, I have two. The first one was early in my career when I developed a non-linear mixed effect model applied to cow growth curves for the USDA, using machine learning and big data before they were known as such.
The second one is the transformation we have been driving at NewDay, including the migration of all of our reports to AWS, the migration to modern analytical, reporting and decisioning tools, the recruitment and development of a ten-plus strong data science team and the delivery of our first machine learning models.
I have had a few key influential role models during my career. One of the key ones was Professor Graydon Bell, who was one of my MSc professors. His charisma, enthusiasm for statistics and experimental design and the encouragement and opportunities he gave me helped me approach, shape and solve all kinds of problems. I am still using some of the tools I learned with him during these formative years.
2019 turned out the way I expected with a clear demand and need for more governance around data and AI, and some regulations starting to emerge. And, unfortunately, the public have been presented more negative aspects of the use of big data and AI (eg Netflix Great Hack). There has also been the failure to meet expectations on solving universal problems, such as disease detection and cure. On the other hand, I have not seen many developments on data democratisation as I would have expected, let’s hope this will accelerate in 2020.
I expect 2020 to be similar in many aspects to the trends observed in 2019, with more and more regulations to comply with and adapt to, and a demand for more transparency.
2020 should also see more democratisation around data and tools to help non-data experts and society become less fearful of data, machine learning, AI, etc… I hope we will see some positive impact to society highlighted and the public starting to be less scared of big data.
I think the biggest opportunity will come from leveraging the data and technology innovations to help solve large world issues, such as global warming or hunger. This would need to be done cross functionally and through total sharing of data, public and private, but this might be utopic.
The biggest challenge lies in the reliability, documentation, real-time accessibility and tracking of data. Data provided by third-parties or generated internally still requires too many quality checks and is not 100% timely to gain full trust. Analysts are still spending too much time trying to understand and access data compared to using data to drive strategies.