Jacky Poon
Head Of Actuarial And Analytics at nib Group
Sydney, New South Wales, Australia
Summary
👤
Senior🎓
Top SchoolJacky Poon is a qualified actuary and analytics leader with 10 years’ experience turning statistical models into commercial outcomes across insurance and travel businesses. As Head of Actuarial and Analytics at nib Group he blends pricing, portfolio management and finance leadership to drive profitability, having previously built analytics functions and pricing infrastructure at Zurich. He champions data literacy across organisations, believing insights only deliver value when stakeholders and frontline staff are engaged to act on them. Jacky also holds governance training from the Australian Institute of Company Directors, giving him a strategic perspective on risk and decision-making beyond pure analytics. An early tutor in mathematics, he brings both deep technical rigour and a practical, people-focused approach to change.
10 years of coding experience
3 years of employment as a software developer
Graduate of the Australian Institute of Company Directors, Graduate of the Australian Institute of Company Directors at Australian Institute of Company DirectorsBachelor of Commerce / Bachelor of Science Actuarial Studies / Mathematics, Bachelor of Commerce / Bachelor of Science Actuarial Studies / Mathematics at Macquarie University
English, Chinese, python, r
Github Skills (50)
cookbook10
data-science10
analytics10
disk-based10
r9
anaconda9
r-package8
qt7
python-development7
language-server-protocol7
ide7
spyder7
jupyter-book7
natural-language-processing6
python6
Programming languages (4)
Github contributions (5)
ActuariesInstitute/cookbook
Sep 2021 - Feb 2023
Data and analytics cookbook for actuaries
Contributions:3 reviews, 536 commits, 33 PRs in 1 year 4 months
analyticscookbookactuarialdata-science
JackyP/penalised-unexplainability-network-payments-per-claim-incurred
Apr 2019 - Jul 2019
A granular machine learning risk pricing and loss reserving model.
Contributions:3 commits, 4 PRs, 9 pushes in 2 months
machine-learning
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