Максим

Maksym Ivashchyshyn
Data Scientist
# [open contact info](look above in the "contact info" section) | ƒ [open contact info](look above in the "contact info" section) | + Ternopil, Ukraine

Education
Ternopil Ivan Puluj National Technical University,Bachelor’s degree in 2021 – 2025
Cybersecurity
Ternopil Ivan Puluj National Technical University, Master’s degree in 2025 – Present
Cybersecurity

Professional Skills
Programming & Data Analysis
• Python: Primary language for statistical analysis, data mining, and building advanced machine learning
pipelines.
• Libraries: Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Seaborn (utilized for complex numerical
computations, data cleaning, exploratory data analysis (EDA), and visualization).
• Database & Data Management: SQL (PostgreSQL), Vector DBs for efficient data retrieval and unstructured text
processing.

Machine Learning & Statistical Modeling
• Regression & Forecasting: Implemented Linear Regression (OLS, Ridge, Lasso) and advanced ensemble
methods like XGBoost for predictive analysis and time-series forecasting (SARIMA, Amazon Chronos).
• Classification Models: Designed and optimized predictive models using Logistic Regression, SVM, Decision
Trees, and K-NN, employing cross-validation and hyperparameter tuning to enhance performance. .

Deep Learning
• PyTorch, TensorFlow: Built and scalable deep learning models, utilizing flexible computational graphs for
custom model training and experimentation.
• Transformers: Applied Transformer-based models for time-series prediction and sequence modeling.

Natural Language Processing & Generative AI
• LLM Agents & RAG: Designed autonomous agent workflows and Retrieval-Augmented Generation systems
using Claude/GPT/Gemini for context-aware analysis.
• NLP Techniques: Utilized deep learning approaches for sentiment analysis and parsing text data.
• Tools: LangChain, smolagents, Vector Databases for semantic search.

Professional Experience
Data Scientist, Amazinum – Ternopil, Ukraine Oct 2024 – Present
Financial Agent Development
• Designed and implemented autonomous financial agents using Claude 4 Sonnet and the smolagents framework.
• Developed a hybrid forecasting engine integrating XGBoost and SARIMA models.
• Built a RAG architecture with ChromaDB to provide AI agents with context-aware access to financial data,
enhancing the accuracy of investment recommendations.
• Automated DCF valuation and portfolio construction using skfolio.
• Deployed ML models and AI services via a high-performance FastAPI backend, utilizing Docker for consistent
environment management and reproducibility.
Intelligent Document Parser
• Built an automated data processing pipeline for LLM fine-tuning, capable of generating structured JSONL
training pairs and cutting manual preparation efforts by 80%
• Built a LLM validation system (Python, LangChain, Gemini) achieving 90%+ accuracy against ground truth data.
• Implemented automated confidence scoring with detailed reasoning generation. Engineered multi-criteria
evaluation prompts achieving high validation accuracy.
• Implemented a pipeline that maps AI reasoning chains directly to extracted data fields for precise explainability.
• Designed a robust prompt architecture for structured outputs.