Position: Data Scientist | Machine Learning Engineer | Big Data Engineer
Category: Data Science
English level: upper
Years of experience: 5

As a diploma project made research in "Automated architecture search of deep neural networks for image classification". Model based on searching CNN architectures within a continuous domain using gradient descent (solving bilevel optimization problem).
I have a Master's degree in Data Analytics. In Data Science for 4+ years. Before worked as a python developer (2 years) and Game developer (developed augmented reality games in Snapchat for 2 years).

Past projects:
- Building databricks workflows for big data to find car signal anomalies. Transform and organize big data from data lake. Build ml pipelines to find anomalies. (Spark, Databricks, mlflow, XGBoost)
- Video recommendation service. 
Developed pipeline on Apache Airflow with the switcher of models: deep recommender and two-step model. (user-content based models), A/B testing
- Service to tag invoices (NLP) 
Build AWS Sagemaker pipeline with different models (classification, week-supervision). 
     Also, I led the team and worked directly with the client on an experimental stream doing various things: PU learning, Multiple-Instance learning, Anomaly Detection, extract not biased subsamples
- Demand forecast for suppliers to optimize logistics.
Developed a flexible pipeline that is able to be trained on different data for different markets and countries. Optimize warehouses logistics (Databricks, Spark)
- Service to recommend an optimal price for hotel rooms to maximize income (time-series)
There was a lot of work customizing architecture for the encoder-decoder model, preventing leaked features (monotonic constraint), and finally extracting the desired features dependence. 
- Predict returning of the customer (Big data, Big query)
- Service for lungs diagnosis. (Computer vision)
The service segment lungs, and is available to detect 10+ diseases, and segment pneumothorax
- Anomaly detection service for scraping company (Semi-supervised)
Complex feature engineering work. (fighting with aggregated-correlated features)
- Service to predict policy price using minimal input data for insurance companies. 
Besides standard work with the model and service, there was done analysis on feature importance, using SHAP and own techniques.
I am currently located in Europe.