Jovan Veljanoski
Lead Data Scientist
Randstad, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolJovan Veljanoski is a Lead Data Scientist with a decade of experience blending astrophysics-grade statistical rigor and practical ML solutions for industry. As co-founder and core contributor to Vaex, he helped build an out-of-core DataFrame that enables billion-row exploration and visualization without cloud or distributed compute, reflecting a deep focus on high-performance data tooling. He has led recommender and ranking systems, A/B testing platforms, and automated analytics pipelines at Tiqets, and built production image-classification and optimization systems in manufacturing and agriculture. His research background driving novel analyses of Gaia’s billion-point catalog underpins strengths in anomaly detection, Bayesian model fitting, and handling highly imbalanced, high-dimensional data. Jovan pairs product ownership and API design with hands-on implementation, mentoring teams while shipping robust pipelines. He is unusually comfortable moving from Monte Carlo MCMC and Gaussian-process tuning to scalable engineering that brings large-data science into production.
10 years of coding experience
8 years of employment as a software developer
Bachelor's degree, Physics with Honors in Astrophysics, First Class, Bachelor's degree, Physics with Honors in Astrophysics, First Class at The University of Edinburgh
English, Macedonian, Serbian, Croatian
Github Skills (11)
data-manipulation10
dataframes10
python10
data-science10
dataframe10
numpy10
data-analysis10
data-visualization8
data-visualisation8
data-visualizations8
machine-learning7
Programming languages (8)
Github contributions (5)
vaexio/vaex
Jun 2017 - Dec 2022
Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀
Role in this project:
Data Scientist
Data ScientistContributions:87 reviews, 458 commits, 381 PRs in 5 years 6 months
Contributions summary:Jovan's contributions primarily focused on enhancing the functionality of the `vaex` library, which centers around the efficient handling of large datasets, and the user’s modifications extended this capability by incorporating additional NumPy functions into the 'select' method of dataset.py. Furthermore, the user implemented new methods for creating and selecting circular and elliptical regions within datasets, adding tests for them, indicating work on enhancing data manipulation capabilities. In addition, the user included a method for virtual column transformations, enabling conversion of data in new ways.
pythonhybrid-corearrowrowsbigdata
JovanVeljanoski/website-hristi
Aug 2018 - Apr 2023
Contributions:9 pushes, 2 branches in 4 years 9 months
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