Paulo Pinto is a Director-level software engineer and data scientist with 11 years of experience applying physics-trained rigor to financial services, currently leading platform modernisation at Morgan Stanley in Frankfurt. He designs and builds cloud-ready, Python-based ETL and reporting platforms deployed as microservices in Docker from a single monorepo, and has driven hygiene and legacy-modernisation initiatives across critical reporting pipelines. His background in computational physics (PhD, UCD) and two postdocs informs strong quantitative modelling skills used to develop anomaly detection systems—work that earned him two patents and founding membership of Morgan Stanley’s Anomaly Detection Working Group. He has hands-on experience running large-scale data acquisition and packet-capture fleets, decoding 14+ market message formats across 40+ exchanges, and implementing models from simple heuristics to deep autoencoders. Known for turning complex, high-dimensional problems into reliable production code, he blends research-grade ML methods with pragmatic engineering to meet tight regulatory and operational demands.
10 years of coding experience
9 years of employment as a software developer
PhD, Physics, PhD, Physics at University College Dublin
Masters, Computational Methods in Science and Engineering, Masters, Computational Methods in Science and Engineering at Universidade do Porto
An asyncio Python library to perform requests against News API
Contributions:1 release, 39 PRs, 74 pushes in 2 years 3 months
python-libraryapipythonasyncionews-api
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