Ashish Saxena
Consulting Account Lead (CAL), Healthcare, Google Cloud
Flemington, New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolAshish Saxena is a Cloud Strategy Leader and Consulting Account Lead for Healthcare at Google Cloud with over nine years of focused experience in cloud, data and AI strategy and more than two decades of hands-on expertise in data integration and ETL. He helps healthcare and life sciences organizations realize measurable cloud value by aligning technical architectures to business drivers and maturing cloud capabilities through strategic partnership and tactical delivery. Earlier roles at Accenture and across healthcare-focused consultancies reflect deep domain knowledge in HCLS data flows, MDM, and analytics for high-impact, regulated environments. Ashish is also a practical ML engineer and back-end contributor to prominent open-source TensorFlow projects—implementing optimization algorithms and quantitative finance features that show his comfort bridging research-grade algorithms and production systems. Based in Flemington, NJ, he combines enterprise delivery experience with a developer’s attention to algorithmic detail, enabling pragmatic, risk-aware cloud transformations.
9 years of coding experience
5 years of employment as a software developer
Duke University - Fuqua School of BusinessBachelor of Engg., Computer Science, Bachelor of Engg., Computer Science at Pune University India
Github Skills (20)
algorithm10
probabilistic-programming10
python10
optimizers10
machine-learning10
tensorflow10
numerical-methods10
optimisation10
quantitative-finance10
optimization10
build-automation9
bayesian-methods9
data-structures8
deep-learning8
data-structure8
Programming languages (2)
Github contributions (5)
google/tf-quant-finance
Aug 2019 - Jan 2023
High-performance TensorFlow library for quantitative finance.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:2 reviews, 80 commits, 36 comments in 3 years 5 months
Contributions summary:Ashish made multiple contributions to the `tf-quant-finance` repository, focusing on improving the library's functionality related to quantitative finance. Their work included implementing features for implied volatility calculations using the Stefanica-Radiocic algorithm and incorporating root search methods. Furthermore, the user addressed build and packaging issues, ensuring the successful creation of the pip package and correct inclusion of third-party data. The commits show a focus on enhancing the codebase's capabilities for financial modeling and improving build processes.
pythonautomatic-differentiationnumerical-optimizationtensorflowfinance
tensorflow/probability
Jun 2018 - Jan 2019
Probabilistic reasoning and statistical analysis in TensorFlow
Role in this project:ML Engineer
ML EngineerContributions:16 commits, 19 comments, 3 issues in 7 months
Contributions summary:Ashish contributed to the `tensorflow/probability` repository by implementing and improving optimization algorithms. Specifically, the user implemented the BFGS optimization algorithm and the Nelder-Mead derivative free optimization method. They also added support for dynamic shapes in the slice sampler and made improvements to the RandomWalkMetropolis, and differential evolution optimizers, addressing potential issues like NaNs, convergence criteria, and parameter handling.
statisticspythonprobabilistic-reasoningdata-sciencedeep-learning
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