Sławek Smyl
Distinguished Data Scientist
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolSławek Smyl is a Distinguished Data Scientist with over a decade of deep expertise in forecasting, blending statistical methods and neural networks to win top international competitions including M4 and IEEE CIF 2016. He has driven forecasting and capacity-optimization work at Walmart, Facebook, Uber and Microsoft, applying probabilistic programming (Stan, Pyro), dynamic neural frameworks (DyNet, PyTorch), and advanced simulation techniques. His research contributions include state-of-the-art time series algorithms that outperform published results on M3 data, several scientific papers, invited talks at ISF and KDD, and three software patents. Comfortable across systems and languages from Fortran to C#/Java and databases like Oracle/SQL Server, he pairs production-grade engineering with academic rigor. A multi-citizen (Poland, Australia, US green card) with degrees in physics and computer systems engineering, he uniquely combines legal studies and simulation expertise to tackle complex, real-world forecasting problems.
10 years of coding experience
23 years of employment as a software developer
Graduate Diploma, Legal Studies, Graduate Diploma, Legal Studies at UNSW AustraliaMaster's Degree, Physics, Master's Degree, Physics at Jagiellonian UniversityMaster's Degree, Computer Systems Engineering, Information Technology, Master's Degree, Computer Systems Engineering, Information Technology at RMIT University
Github Skills (59)
r-package10
python10
machine-learning10
cntk10
java10
numpy10
bayesian10
deep-learning10
gpu10
neural-network10
probabilistic-programming9
variational-inference9
pytorch9
gluonts9
exponential-smoothing9
Programming languages (3)
Github contributions (5)
cbergmeir/Rlgt
Jan 2017 - Jul 2019
Rlgt is an R package for Bayesian Exponential Smoothing
Contributions:72 commits, 20 pushes, 4 comments in 2 years 7 months
bayesianexponential-smoothingrr-package
slaweks17/ES-dRNN
Jan 2022 - Dec 2022
Short Term Load Forecasting with ES-dRNN
Contributions:7 commits, 6 pushes, 1 branch in 10 months
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