Borong Zhang
Sr Staff Analytics Engineer at Flex
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolBorong Zhang is a Sr. Staff Analytics Engineer based in New York with a focused track record of turning data into production-ready solutions across fintech and retail environments. With roles advancing from technical analyst to analytics engineering manager and now senior staff at Flex, he blends hands-on engineering, credit strategy analytics, and BI to drive measurable business impact. He contributes to open-source SafeRL infrastructure (OmniSafe), fixing environment issues and improving object-oriented design—an indicator of practical ML engineering chops beyond traditional analytics. Borong holds an MS in Business Analytics and a BS in Mathematical Sciences, pairing quantitative rigor with product-minded execution. Known for tackling messy data and shipping robust pipelines, he brings a systems-level view that bridges research, engineering, and business outcomes.
3 years of coding experience
5 years of employment as a software developer
Bachelor of Science (BS) Mathematical Sciences, Bachelor of Science (BS) Mathematical Sciences at Worcester Polytechnic InstituteMaster of Science - MS Business Analytics, Master of Science - MS Business Analytics at Wake Forest University
English, Chinese
Github Skills (9)
deep-reinforcement-learning10
pytorch10
machine-learning10
python10
reinforcement-learning10
deeplearning-ai9
deep-learning9
benchmarking8
benchmark8
Programming languages (2)
Github contributions (5)
PKU-Alignment/omnisafe
Dec 2022 - Mar 2023
JMLR: OmniSafe is an infrastructural framework for accelerating SafeRL research.
Role in this project:
Back-end Developer and ML Engineer
Back-end Developer and ML EngineerContributions:1 release, 194 reviews, 18 commits in 3 months
Contributions summary:Borong primarily contributed to the `omnisafe` framework, focusing on the core infrastructure for SafeRL research. Their work involved fixing environment-related issues, such as warnings in the environment wrapper, and adding tests for render modes. The user also refactored code for improved object-oriented programming style. Additionally, they worked on the addition of new tasks, supporting the circle and run environments.
pytorchbenchmark-suitedeep-learningreinforcement-learningsafe-reinforcement-learning
PKU-Alignment/safety-gymnasium
Jan 2023 - Mar 2023
NeurIPS 2023: Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
Contributions:5 releases, 23 reviews, 16 commits in 1 month
constraint-rlconstraint-satisfaction-problemreinforcement-learningsafe-policy-optimizationsafe-reinforcement-learning
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