Episode 6: Actuaries in Data Science with Daniel Stone
“I often quote the Field of Dreams with Kevin Costner. "If you build it they will come". And I 100% believe in building very strong foundations; whether it’s data, whether it’s skills, whether it’s the tools or the time and space to get things done. Because, once you build it, it will come.”
Daniel Stone,
Data Science Actuary
Daniel Stone is a qualified actuary and data scientist with 20 years of experience in a wide range of fields including varied-sized life offices, reinsurance, consulting and expert-level model building experience. He has worked in Australia, South Africa, United Kingdom and ASEAN.
His recent experience is in applying big data and cloud technologies, data engineering automation and the application of statistical and data science techniques in life insurance and actuarial pricing.
In his spare time he enjoys spending time at the beach with his family and making a deep run in poker tournaments.
In this interview I asked Daniel:
How he became on of the first life insurance actuaries in Australia who adopted data science into their roles,
His advice to experienced actuaries that are wondering if it’s “too late” to learn data science and apply it to their roles,
How he defines good leadership,
How he balances the need to deliver without overloading team members,
What skills he looks for when hiring in his teams,
How he sees the competitive landscape between actuaries, data scientists and other data analytics professionals, and
His top tip for actuaries wanting to improve their skills in data science.
Connect with Daniel on LinkedIn.
In the episode, Daniel mentions: Darren Wickham, Francis Burgess and Andrew Yin.
Daniel referred to a range of learning options for actuaries wanting to build their data science skills, including:
High level knowledge: Datacamp R, Python, SQL and Pluralsight
Data Science specialisation: Udacity
Most major universities: masters courses
Cloud & Big Data: Microsoft Azure Fundamentals, Microsoft Azure AI Fundamentals and Databricks
