Position: Data Scientist / Machine Learning ML R&D Engineer
Category: Data Science
English level: fluent
Years of experience: 5

- by adding improvements to the novel anomaly detection model I was able to reduce false negative rate by 30%
- by designing an AutoML pipeline framework, I was able to decrease hyperparameters tuning time from ~5.5h to ~30m
- mentorship and technical interviewing experience
As ML Engineer, I've worked on search and ranking using Python, AWS, ELK stack, and Airflow.

As Data Scientist on another project, I was responsible for data analysis and MVP prototyping of data-heavy products for a client operating in the mining industry. Technologies utilized were Python, GCP, and Airflow.

On another project, I've designed scalable data processing and modeling pipeline architecture. I also did research and implementation of novel methods of Outlier Detection, Hyperparameters Optimization and Unsupervised Anomaly Detection in Time Series data.
Technologies:
Python, MATLAB, pytorch, scikit-learn, pyod, ray, AWS S3, EC2, ECR

As a Middle Machine Learning Engineer, I've worked on personalised recommendation system for adtech product company. Technologies: Python, pytorch, catboost, scikit-learn, plotly, AWS S3, EC2, ECR

The first title I had was Junior Computer Vision Engineer, where I was responsible for data preprocessing, visualization and statistical modeling. I have also worked on a camera-to-LiDAR auto-calibration, Driver Face Recognition and Gaze Tracking, as well as Multi-object tracking on LiDAR data.
Technologies:
C++, OpenCV, Python, pytorch
I am mostly interested in:
- Recommendation systems and search
- Anomaly detection and predictive maintenance
- Time series analysis

If the project or idea is interesting, I am capable of working with:
- Computer Vision
- NLP

I will ignore the following domains:
- Data Engineering