Position: Data Scientist
Category: Data Science
English level: intermediate
Years of experience: 2.5

Deployment end-to-end ML models. Cover code with tests, creating documentation.
Machine Learning engineer with 2,5 years of experience using Python.
From July 2020 work as Data Scientist with key responsibilities in development and monitoring ML models. Analyzing customers data and creating analytics reports.

Practical skills ->
- Skills of analysis, research and evaluation of data quality;
- Finding correlations, data distribution, and samples;
- Conducting statistical experiments, testing the significance of statistical hypotheses;
- Regression and classification algorithms;
- Application of statistics in machine learning.

Machine Learning stack:
- SkLearn - building pipelines, forming train\test\val samples;
- NumPy - mathematical operations;
- Matplotlib - data visualization;
- Pandas - processing, loading and manipulation of data.

ML algorithms:
- XGBoost | LightGBM | Catboost | randomForest && Metrics: Accuracy, precision, recall, AUC-ROC, Gini - PSI - monitoring of production models
Deep learning:
- Basic understanding of the principles of building neural networks, TensorFlow implementation, PyTorch.

Python:
- OOP - splitting ml tasks into classes, writing libraries
- GeoPandas + OpenStreetMap - visualization of geo-analytics, data processing, building recommendations based on client data;
- Folium - html map rendering, heat maps, geoanalytics;

Docker.
Data Engineering
SQL in the SAP environment;
Propensity model match;
Git - CI\CD
R
Libraries for stat. data analysis and visualization, cleaning of training datasets, end-to-end data analysis

Other -> TypeScript, Node.js, React, SASS