Andrew Warren
Consulting Actuary at Milliman
Bellevue, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Warren is a Consulting Actuary with 15 years of experience specializing in life insurance valuation and GAAP reporting, currently implementing cloud-based Milliman Integrate solutions to help clients meet LDTI challenges. He brings deep technical modeling expertise across Universal Life, Variable and Indexed products as well as annuities, and is proficient in MG-ALFA, Milliman Integrate, Excel/VBA, Power BI, Microsoft Dataflows, SAS, and Access. At Milliman he progressed from Associate Actuary to Consulting Actuary, combining hands-on model development with client-facing implementation and governance. He is pursuing the Life Insurance track for the FSA, underscoring his commitment to advanced actuarial credentialing. Unusually for a life actuary, he has contributed to a well-known open-source deep learning project (Caffe2), improving shape inference and synchronization primitives, reflecting a strong interest in scalable engineering and ML tooling. Based in Bellevue, Washington, he blends mathematical rigor from a BA in Mathematics and Economics with practical, production-focused actuarial technology delivery.
10 years of coding experience
15 years of employment as a software developer
Bachelor of Arts, Mathematics, Economics, Bachelor of Arts, Mathematics, Economics at Calvin University
Github Skills (15)
data-parallel10
machine-learning10
data-parallelism10
caffe10
develop10
deep-learning10
c-language10
cprogramming-language10
python10
gloo10
operator10
ai9
artificial-intelligence9
distributed-systems9
tensorflow8
Programming languages (5)
Github contributions (5)
facebookarchive/caffe2
Feb 2017 - Feb 2018
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:24 commits, 3 PRs, 1 comment in 1 year
Contributions summary:Andrew primarily worked on improving the shape inference capabilities of the Caffe2 deep learning framework. Their contributions include adding support for external input blobs and implementing shape and type inference from a map. They also added tests to verify shape inference in operator unit tests. Furthermore, the user implemented a synchronization barrier API for data parallel models and added an optional timeout parameter to Gloo ops.
pytorchscalablecaffe2deep-learningml
andrewwdye/flytekit
Sep 2022 - Oct 2023
Extensible Python SDK for developing Flyte tasks and workflows. Simple to get started and learn and highly extensible.
Contributions:6 releases, 14 pushes, 3 branches in 1 year
flytepythondevelopingflyte-tasksget-started
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