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Work experience

Data scientist

from 01.2024 to 04.2026 (2 years 4 months)
Onicore, Дистанційно (Фінанси, банки, страхування)

Onicore — Quant / Trading / Data Science Engineer — 2.3 years
Worked on algorithmic trading, machine learning, and data science systems in crypto markets.
Covered full cycle from research and strategy design to execution, backtesting, and live deployment across spot, futures, and options.
• Built real-time trading systems across spot, futures, and options
• Worked with tick data, aggregated trades, order book snapshots, and custom backtesting
• Developed order book analytics: liquidity heatmaps, support/resistance, imbalance signals
• Implemented modified Avellaneda–Stoikov models with inventory-aware hedging
• Worked on spot-futures arbitrage, delta-neutral setups, and options hedging
• Explored strategies based on market structure, time series, anomaly detection, clustering, and forecasting

• Built end-to-end trading systems for live crypto execution
• Worked across crypto / blockchain markets on major CEX venues including Binance, WhiteBIT, and Deribit
• Developed market-making and order book-driven strategies with inventory-aware logic
• Worked deeply with order book data, liquidity heatmaps, imbalance signals, and microstructure-based ideas
• Built custom high-fidelity backtesting workflows using tick and order book data
• Applied derivatives logic across spot, futures, and options, including hedging and delta-neutral setups
• Used ML / machine learning and data science for anomaly detection, clustering, prediction, and forecasting
• Had adjacent exposure to AI tooling and selective OpenAI API usage

Data scientist

from 04.2018 to 12.2023 (5 years 9 months)
PIVIST, Київ (IT)

PIVIST LLC — Quant / Trading / Data Science Engineer — 5.75 years
Worked on quantitative models and trading research across energy, commodities, equities, ETFs, and macro markets.
Focused on machine learning, deep learning, statistics, probability, time series, and predictive modeling for trading signals.
• Worked with tick data, custom bars, and aggregated market data
• Built predictive and forecasting models using gradient boosting, tree methods, deep learning, and statistics
• Designed features using rolling stats, extrema logic, zig-zag, Fibonacci, feature importance, selection, and target engineering
• Applied clustering, regime detection, anomaly detection, and dimensionality reduction
• Worked with ARIMA, SARIMAX, GARCH, options-related modeling, and order book research
• Built NLP pipelines for news and macro-event analysis, including sentiment, NER, TF-IDF, BERT, and FinBERT

PIVIST LLC (finTech):
• Developed predictive and forecasting models for financial time series across Brent / WTI futures, energy, commodities, stocks, ETFs, and derivatives
• Built research pipelines combining ML, machine learning, deep learning / DL, NLP, and statistics
• Applied gradient boosting, tree-based models, ensemble approaches, and deep learning in trading research
• Improved robustness through feature engineering, anomaly filtering, regime detection, and target engineering
• Worked with richer market representations beyond OHLCV, including custom bars and derived features
• Contributed to production-oriented integration of research models and signals
• Built NLP pipelines using sentiment, NER, BERT, FinBERT, spaCy, and NLTK
• Had secondary exposure to text-focused NLP / LLM workflows

Математик

from 10.2017 to 03.2018 (6 months)
Радіонікс, Київ (IT)

Radionix — Mathematician / Software Developer — 6 months
Worked on applied math, signal processing, and software tasks in defense-related systems.
• Worked on object detection across video, infrared, and radio spectrum data
• Applied signal filtering and noise reduction
• Worked on trajectory and motion modeling based on differential equations
• Participated in early CV / OpenCV and classification tasks

Radionix (milTech/defTch):
• Applied mathematical and programming skills to real defense-related tasks
• Worked with multi-spectrum data including video, infrared, and radio inputs
• Gained early practical experience in signal processing, object detection, classification, and trajectory modeling
• Used OpenCV and computer vision methods in research and implementation
• Built an early foundation in applied math, defense-related systems, and engineering work

Лаборант

from 04.2016 to 12.2016 (9 months)
Київський авіаційний інститут, Київ (Освіта, наука)

National Aviation University — Laboratory Assistant — 9 months
Worked in an academic environment while studying Applied Mathematics.
• Supported laboratory, technical, research, and analytical tasks
• Worked with mathematical methods, numerical computing, and image processing
• Participated in early OpenCV / computer vision work
• Contributed to academic and applied research support

National Aviation University:
• Combined academic work with practical technical and research support
• Participated in student scientific activity related to image processing and mathematical methods
• Built a foundation later supporting CV / computer vision and quantitative research
• Strengthened research discipline, analytical thinking, and technical writing through academic practice
• Contributed to early UAV / Drone-related visual processing

Education

National University «Kyiv Aviation Institute»

Прикладна математика, Київ
Higher, from 2017 to 2019 (2 years)

Master’s Degree in Applied Mathematics

Master’s thesis:
«Інформаційна технологія кластеризації типів місцевості та пошуку об'єктів за даними аерофотозйомки»

Focus areas:
• aerial imagery analysis;
• terrain-type clustering / classification;
• object search in aerial images;
• digital image preprocessing;
• segmentation and recognition tasks for imagery captured from airborne platforms.

Tech stack: Python, C#, MATLAB, NumPy, pandas, OpenCV, scikit-learn, statsmodels, image processing, computer vision fundamentals, pattern recognition, mathematical modeling, applied statistics, optimization.

National University «Kyiv Aviation Institute»

Прикладна математика, Київ
Higher, from 2013 to 2017 (4 years)

Bachelor’s Degree in Applied Mathematics

Bachelor’s thesis:
«Метод та інформаційна технологія розпізнавання об’єктів з камери безпілотного повітряного судна»

Focus areas:
• object recognition from UAV camera imagery;
• digital image analysis;
• early computer vision methods;
• applied mathematical methods for image-based detection tasks.

Selected academic activity:
• participant of university and national student mathematics olympiads;
• 1st place among first-year students in an internal NAU mathematics olympiad;
• participant of the II stage of the All-Ukrainian Student Mathematics Olympiad (2016);
• participant in student scientific work related to image processing and B-spline image models;
• participant of POLIT student scientific conference.

Selected publication / conference contribution:
«Детектор кривої рівня масштабування на основі В-сплайн-моделі зображення».

Tech stack: C, C++, C#, MATLAB, image processing, object recognition, mathematical modeling, numerical methods

Knowledge and skills

• Python
• Machine Learning
• Data Science
• Pandas
• Git
• Deep Learning
• Scikit-learn
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Statistics
• TensorFlow
• Keras
• PyTorch
• Python Requests
• Docker
• PostgreSQL
• FastAPI
• Online sales
• REST API
• Knowledge of NLP techniques
• Computer vision
• Cryptotrading
• Xgboost
• Lightgbm
• JSON
• Redis
• ClickHouse
• Debian
• Користувач ОС Linux
• TradingView
• Jupyter Notebook
• MS Excel

Language proficiencies

• Ukrainian — fluent
• English — average