Position: Data Scientist/ML Engeneer
Category: Data Science
English level: upper
Years of experience: 1

Took part in several hackathons and events where my team and me came up with projects at MVP stage and `ve been assigned a finalist status. (see pdf CV for more info):
- Megogo and Parimatch Hackathon (Voice Assistant App for managing media content)
- Machine Learning Week by NTUU KPI x AI House x Pawa x Reface (Sports Broadcasts Summarization App, Optical Character Recognition Doc Scanner App)
- AI for Ukraine lectures & workshops by AI House

Currently I'm studying for a master's degree in NTUU “Igor Sikorsky Kyiv Polytechnic Institute” at the Faculty of Applied Mathematics. 
In my bachelor's thesis "Сompany derivatives forecasting using deep learning" I used BERT model for financial news` sentiments estimation and seq-to-seq RNN LSTM for time series forecasting.
In scientific paper "Comparative analysis of approaches and methods for evaluating the sentiment of financial information about the current state of the company" performed comapison of dictionary-, ML- and DL-based approaches and focused on deep learning methods like LSTM with GloVe embeddings, LSTM with ELMo, ULMFiT and BERT in order to perform sentiment analysis NLP task in specific area of experience.

Languages:
English (b2)
German (b1)
Russian (Native)
Ukrainian (Native)
Data Scientist at DataObrii, consulting firm that specializes in data science, ML and AI. Based in London, UK

— Pricing optimization for Swyfft insurance company

— Dual-use object tracking / object detection system for drone onboard inference

— Inhouse tasks


Data scientist in Beehiveor consulting r&d company that provides data-driven solutions based on neuromarketing and behavorial research for 1 ye 3 mo. Based in Kyiv, Ukraine

— Participate in development of EyePass for DTEK – software that uses a webcam to track user's eye-movements and head position for gaze coordinates determination. 
(Creating additional CV features: facial landmarks, head position, distances /  feature importance estimation, dimension reduction (AEs, t-SNE, umap) / architecture selection to improve model performance / building and hyper-tuning LSTM, tree, XGBoost, multi-input CNN-based models for face & eyes recognition, blink detection, gaze point determination and cheat detection; ensembling models  / implementing personal calibration improved accuracy dramatically (two approcahes: 1) fine-tuning CNN, 2) external ML model + geometry))

— EEG/ERP research on target audience reaction to the ads video, website interface and goods packages design 
(processing signals with ICA, FFT, smoothing / determining AOIs, gaze trajectory, EEG and eye-tracking metrics / AB testing / clustering with KMeans, DBSCAN, fuzzy CMeans )
Cases:
+ Injoy alcodrink promo video research
+ PariMatch website usability analysis
+ Wine labels from different brands research

— EyePass Survey for online education platform
(model performance monitoring / NLP (NER, sentiment analysis), defining reading AOIs / gaze trajectory analysis, eye-tracking metrics / special EyePass Metrics development)


Technology stack and skills:
- Python ( pandas, numpy, scipy, librosa, sklearn, pycaret, tensorflow, keras, xgboost/catboost, pytesseract, opencv, mediapipe, skopt, optuna, statsmodels, b-soup etc)
- ML/DL algorithms, ensembling
- Time series, signal processing
- Visualization (matplotlib, seaborn, plotly)
- CV, NLP
- AWS (Sagemaker, Forecast, Rekognition, Groungd Truth, Comprehend, S3)
- Oracle SQL, PostgreSQL, MongoDB
- Hadoop + MapReduce + pySpark, KNIME
- Apache Superset
- Streamlit, Dash
- Docker
- RESTful API (uvicorn + fastAPI/flask + Streamlit/Dash)
- Git
- Matlab
- Strong knowledge of math and statistics
- MS Office