Stan Maydan
- Role
- Strategic Quantitative Risk Research at Bloomberg
- Location
- Bronx, NY, US
- LinkedIn followers
- 500 followers
About Stan Maydan
Risk Management, Scenario Analysis, Quantitative Finance, Generative AI in Finance Expert
Experience
Strategic Quantitative Risk Research
Aug 2010 — Present
Development of Value at Risk, Stressed Value At Risk, Expected Shortfall, Stress Testing/CCAR/EBA, climate risk, credit curve building applications for Enterprise Risk Management Clients of Bloomberg LP. Deep Learning in Financial Applications). Familiar with various Machine Learning Methods, such as Multivariate Singular Spectrum Analysis (MSSA), Kernel Methods For Pattern Analysis, Cluster Analysis, Neural Networks, Deep Learning Techniques (Convolutional Neural Networks, Autoencoder with Tensorflow, Transformers), Reinforcement Learning, Support Vector Machines (including kernel methods), Kalman Filter (including Unscented version of Kalman Filter), Computational Bayesian Methods utilizing Markov Chain Monte Carlo, Ensemble Machine Learning, Outlier Detection, Wavelet Analysis, Random Matrix Theory, etc. Expert in Matlab, python, numpy, scipy, pandas, statsmodels, scikit learn, matplotlib, tensorflow, pymc3, C#, C++, SQL
Education
Columbia University
Master of Science, Operations Research
Peter the Great St.Petersburg Polytechnic University
Diploma of Engineer Physicist, Solid state physics
Skills
- C++
- Interest Rate Derivatives
- Market Risk
- Kalman Filtering
- Structured Products
- Signal Processing
- Financial Risk
- Quantitative Analysis
- Credit Derivatives
- Hedge Funds
- Quantitative Modeling
- Fx Options
- Abs
- Mbs
- Quant
- R
- Quantitative Investing
- Oracle
- Regulatory Capital
- Computational Bayesian Modeling
- Machine Learning
- Equity Derivatives
- Options
- Bloomberg
- Quantitative Analytics
- Perl
- Portfolio Management
- Financial Engineering
- Sql
- Basel Iii
- Sas
- Var
- Basel Ii
- Python
- Electronic Trading
- Swaps
- Hedging
- Risk Management
- Matlab
- C#
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