Andrey Gaskov
Machine Learning Engineer at Huawei
Novosibirsk, Novosibirsk, Russia
Summary
👤
Senior🎓
Top SchoolAndrey Gaskov is a machine learning engineer with about a decade of experience applying data-centric engineering and statistical modeling to production systems across transportation, aviation, and energy. He has led end-to-end ML work—from presales and domain discovery to deployment—delivering cost and accuracy gains such as a 35% reduction in incorrect arrival predictions and an NLP-driven anomaly detection service for S7 Airlines. His background spans Spark/Scala, Databricks, Azure, .NET/C#, SAS and Delta Lake, enabling scalable data pipelines and model-driven operational improvements. He has deep experience collaborating with domain experts in oil & gas and transit, converting complex process indicators into actionable predictive maintenance and operational models. An active contributor to educational ML resources, he added practical notebooks to a community implementation of The Elements of Statistical Learning that demonstrate applied statistical workflows. Based in Novosibirsk, he combines strong theoretical ML foundations with hands-on engineering to move models reliably into production.
8 years of coding experience
10 years of employment as a software developer
Master's degree, Information Technology, Master's degree, Information Technology at Novosibirsk State University (NSU)
Github Skills (7)
linear-regression10
python10
statsmodels10
data-analysis10
statistical-models9
jupyter-notebook9
machine-learning9
Programming languages (4)
Github contributions (5)
empathy87/The-Elements-of-Statistical-Learning-Python-Notebooks
Jan 2019 - Jan 2020
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
Role in this project:
Data Scientist
Data ScientistContributions:117 commits, 217 pushes, 1 comment in 11 months
Contributions summary:Andrey contributed to the Jupyter notebooks containing the "Prostate Cancer" example by adding examples with code differences, showcasing the analysis of prostate cancer data. These edits demonstrate the application of various statistical and machine learning techniques, including linear regression, model fitting, and the use of tools like `statsmodels` for statistical analysis. The primary focus of their contributions was on data analysis and model application within the domain of statistical learning.
dtwpythonknn-classificationdata-scienceelements
empathy87/storytelling-with-data
Aug 2018 - Jan 2020
Plots from the book "Storytelling with data" implementation using Python and matplotlib
Contributions:14 commits, 41 pushes, 1 branch in 1 year 5 months
pythonmatplotlibplottingstorytellingplots
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