Stylianos Paraschiakos
Data Scientist (Data Product Focused Role)
Randstad, Netherlands
Summary
👤
Senior🎓
Top SchoolStylianos Paraschiakos is a data scientist with 11 years of experience specializing in multimodal time-series and wearable-sensor modeling, currently leading analytics and data-product initiatives at AssistiveWare. He designs reproducible ML pipelines, longitudinal behavioral metrics, and internal analytics products that inform product, UX, and research decisions across organizations. His PhD work and consulting engagements blend physiological-signal analysis with practical product engineering—covering beat-level biosignal processing, signal-quality assessment, and subject-wise validation for digital biomarkers. Comfortable bridging academia and industry, Stylianos also runs a consultancy delivering end-to-end workflows and governance for health-tech teams. A less obvious strength is his practice of embedding reproducibility and instrumentation standards (Git-based ETL/ELT, documentation portals) into product analytics so insights scale reliably across stakeholders.
11 years of coding experience
6 years of employment as a software developer
Mathematics and Computer Science, Mathematics and Computer Science at KU LeuvenDoctor of Philosophy - PhD Machine Learning & Physiological Signal Analysis, Doctor of Philosophy - PhD Machine Learning & Physiological Signal Analysis at Leiden UniversityBachelor’s Degree Pure and Applied Mathematics, Bachelor’s Degree Pure and Applied Mathematics at University of Ioannina
Greek, English, German
Github Skills (10)
recurrent6
arxiv6
energy5
deep-learning3
machine-learning3
architecture3
testing2
neural-network2
python1
security1
Programming languages (2)
Github contributions (5)
parastelios/ClaRe-Dashboard
Sep 2016 - Nov 2022
Contributions:54 commits, 52 pushes, 1 branch in 6 years 2 months
parastelios/GOTOV_PAEE_publication
Nov 2021 - Jan 2023
Notebooks to reproduce the results of publication "A Recurrent Neural Network Architecture to Model Physical Activity Energy Expenditure in Older People" https://arxiv.org/abs/2006.01169
Contributions:3 PRs, 13 pushes, 2 branches in 1 year 2 months
arxivabsdeep-learningexpendituremachine-learning
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