• Python. Libraries: Библиотеки: Pandas, NumPy, sklearn, hyperparameter optimization (Optuna, keras_tuner), xgboost, Keras/ TensorFlow, Matplotlib, SciPy, StatsModels, Jupyter Notebooks, etc.


• R. Packages: tidyverse, ggplot.


• Machine learning:
• Regression: linear, nonlinear (polynomial, spline), regularization (lasso, ridge)
• Classification: logistic regression, KNN, SVM
• Tree-based methods: random forest, boosting
• Deep learning (CNN, RNN)
• Cross-validation, bootstrap
• Clustering
• Data transformation (PCA, SVD)
• Ensembles, pipelines


• Statistics:
• Hypothesis testing
• Parametric and non-parametric methods
• ANOVA
• Multiple testing
• Time series


• SQL (complex queries, window functions, common table expressions)
• Git
• Linux (bash)
• Docker
• Other: REST API

• Pet projects (2020 – present time):


• Training a neural network to distinguish between human translation and machine translation
• Predicting oil and gas production
• Recovering a function from a scanned plot
• Kaggle competition. Uplift modeling — predicting which customers will buy a product if and only if they receive an SMS, i.e. those who won't buy unless they receive an SMS.
• Statistical analysis of usage of phrases in Google Books
• Prototype of a recommender system
• Forecasting bank client churn
• Airbnb price prediction
• Image classification: uninfected cells and cells parasitized by malaria
• Testing the validity of Zipf's law
• More


• Previous experience: translator and interpreter

Higher education: Faculty of Physics, Moscow State University. Degree: diploma of higher education (equivalent of Master of Physics).

Uplift Modeling course at Open Data Science (December 2022).

Data Science reskilling course. Tomsk State University.

Achievements:

• I was the first to complete training out of ~160 students
• Students learning faster presented their projects to other students to help them progress on the course. I presented four projects ouf of 16
• As a top student, I also checked other students' graduation projects (normally done by staff)