Mahdi Davari
Lead Data Scientist Developer
New York, United States
Summary
👤
Senior🎓
Top SchoolMahdi Davari is a Lead Data Scientist and Developer in New York with 9 years of experience building production ML/AI systems and scientific software that bridge materials discovery and enterprise applications. He combines deep academic expertise in computational physics—authoring widely used tools like the FPTE package and contributions to USPEX—with hands-on delivery of containerized ML services, computer vision, and NLP pipelines at firms such as NYSE and Citi. Mahdi has taken research ideas through publication and grant-winning projects into working POCs and deployed solutions, including a Cascade Mask R-CNN table extraction pipeline achieving >97% detection accuracy and patent-pending GUI extraction work. Comfortable across cloud platforms, big-data stacks, and model deployment (Azure, AWS, Databricks, OpenShift, Docker, Jenkins), he frequently productionalizes prototypes within weeks. Colleagues value his blend of rigorous scientific thinking, practical engineering, and ability to lead multicultural teams under tight deadlines. A less obvious strength is his track record of turning advanced evolutionary-computation research into reusable software artifacts adopted by the research community worldwide.
9 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Condensed Matter Physics, Bachelor's degree, Condensed Matter Physics at University of IsfahanMaster's degree, Condensed Matter and Materials Physics, Master's degree, Condensed Matter and Materials Physics at Isfahan University of TechnologyData Science career track, Data Science career track at DataCampDoctor of Philosophy (Ph.D.), Computational Phys/Chem, Doctor of Philosophy (Ph.D.), Computational Phys/Chem at Stony Brook University
English, Persian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
jupyterlab-extension9
dft9
ab-initio8
jupyter8
jupyterlab8
ai-agent6
visualization2
Programming languages (1)
Github contributions (5)
MahdiDavari/FPTE
Jun 2018 - Feb 2022
The FPTE package is a collection of tools for finite pressure temperature elastic constants calculation. Features include, but are not limited to stress-strain method for getting second order elastic tensors using DFT package VASP as well as, ab initio molecular dynamic method for temperature dependent elastic constatns. The package is free and open-source, available on Github.
Contributions:4 releases, 98 commits, 2 PRs in 3 years 8 months
ab-initiodft
MahdiDavari/Data-Science-Portfolio
Jan 2019 - Oct 2019
Contributions:44 pushes, 1 branch in 9 months
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