SkanderĀ Soltani
Sr Research Analyst, Quantitative Solutions (Data Science)
New York, New York, United States
Summary
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Seniorš
Top SchoolSkander Soltani is a senior quantitative researcher and data scientist with nine years of experience designing portfolio construction and optimization solutions, currently leading research efforts in Quantitative Solutions at Charles Schwab in New York. Trained in computational finance at Carnegie Mellon and certified in machine learning engineering, he blends academic rigor with production-focused ML pipelines to improve multi-asset investment processes. His background spans applied research, from ML correlation estimation at Research Affiliates to building end-to-end classification and network analytics for climate-sentiment projects, showing an ability to translate novel techniques into business-ready tools. Known for bridging statistical theory and engineering, he delivers interpretable, scalable models that support portfolio decision-making and risk management.
9 years of coding experience
4 years of employment as a software developer
City University of New York-Baruch CollegeMachine Learning Engineer, Machine Learning Engineer at FourthBrainMaster of Science - MSc, Computational Finance, Master of Science - MSc, Computational Finance at Carnegie Mellon University
English, French, Arabic
Github Skills (9)
statistics9
machine-learning8
python8
data-science7
data-analysis7
scikit-learn6
foundation5
scikit5
fairness-ml4
Programming languages (1)
Github contributions (5)
SSoltani83/BS_PDE_Implied_volatility
Dec 2016 - Jan 2018
Contributions:12 pushes, 1 branch in 1 year
SSoltani83/Numerical_integration_Methods
Dec 2016 - Dec 2016
Contributions:9 pushes, 1 branch in 8 days
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