Vahid Moosavi
Data Scientist And Data Engineer
Zürich Metropolitan Area Switzerland
Summary
👤
Senior🎓
Top SchoolVahid Moosavi is a data scientist and data engineer with 11 years of experience building enterprise-grade, data-driven products at the intersection of machine learning, risk management and urban analytics. Based in Zürich, he blends research-grade expertise from a PhD at ETH Zürich and senior research roles with hands-on delivery at Swiss Re, shaping cloud-enabled architectures and agentic AI use cases for insurance, sustainability and financial risk. He has deep experience in data architecture, stakeholder-facing product roles (including PO responsibilities), and pragmatic technology choices across Azure, AWS, Databricks and Posit. An active open-source contributor, Vahid extended the SOMPY Python library with clustering and hitmap visualizations, reflecting a strong focus on interpretable ML and exploratory analysis. His background in computational urban modeling and planetary-scale spatial ML gives him a rare lens for connecting city-scale data insights to commercial risk products.
12 years of coding experience
9 years of employment as a software developer
Amirkabir University of TechnologyDoctor of Philosophy (Ph.D.), Data Driven Urban Modeling, Doctor of Philosophy (Ph.D.), Data Driven Urban Modeling at ETH ZürichBachelor, Industrial and Systems Engineering, Bachelor, Industrial and Systems Engineering at Isfahan University of Technology
Github Skills (10)
scikit-learn10
data-visualizations10
data-visualization10
data-visualisation10
python10
clustering10
matplotlib10
scikit10
machine-learning9
pandas8
Programming languages (7)
Github contributions (5)
sevamoo/SOMPY
Aug 2014 - Apr 2022
A Python Library for Self Organizing Map (SOM)
Role in this project:
Data Scientist
Data ScientistContributions:129 commits, 54 PRs, 108 pushes in 7 years 8 months
Contributions summary:Vahid primarily contributed to the development of the `SOMPY` library, adding key functionalities such as clustering and hitmap visualizations. Their work involved modifying the core `SOMPY.py` file to incorporate these features, indicating a focus on extending the library's capabilities for data analysis and visualization. The commits also suggest a deeper understanding of data science concepts, as the user implemented features like hitmaps and clustering, which are common techniques used in exploratory data analysis and machine learning model interpretation.
pythonself-organizing-map
sevamoo/data_driven_modeling_2017
Oct 2017 - Nov 2017
Contributions:13 commits, 12 pushes, 1 branch in 1 month
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