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Quantitative analyst
- Considering positions:
- Quantitative analyst, Data scientist, Data analyst, Data engineer, Financial analyst
- City of residence:
- Kyiv
- Ready to work:
- Remote
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CONTACT
Daniil Hutsol Phone: [open contact info ](look above in the "contact info" section)
Quantitative Methods Student, SGH Warsaw School of Economics Email: [open contact info ](look above in the "contact info" section)
Location: Warsaw, Poland
SUMMARY
Quantitative Methods and Information Systems student who has spent the past year turning real-world data into working models, from
predicting corporate bankruptcy to optimizing investment portfolios.
RELEVANT EXPERIENCE & PROJECTS
Math Tutor | Private students & peer exam support, 2026
Tutored high school students in mathematics; supported university classmates preparing for Linear Algebra and Probability Theory
exams.
Portfolio Optimizer | Python, SciPy, yfinance
150+ ETF universe, 2020–2026, train/test split
Built a portfolio optimization pipeline combining Monte Carlo simulation with numerical optimization for a barbell asset allocation
strategy:
Simulated 300,000 candidate portfolios via Monte Carlo, then refined with SciPy SLSQP to maximize Sharpe ratio under barbell
allocation constraints (65–75% conservative, 25–35% high-yield), fitting weights on a training period and validating on a held-out test
period
Achieved an out-of-sample Sharpe Ratio of 1.09 (17.5% annualized return, 11.9% volatility, -10.1% max drawdown) on the test set,
a 9.7% degradation from the 1.21 in-sample Sharpe, indicating limited overfitting
Evaluated downside risk via Sortino, Calmar, and VaR/CVaR across an automated, 150+ ETF universe pulled and filtered via
yfinance
Bankruptcy Prediction System | Python, XGBoost, Optuna, SHAP
Polish Companies Dataset (2000–2013)
GitHub: github.com/lzanam1/bancrucy
Built an ML pipeline to predict corporate bankruptcy for bank credit risk screening:
Achieved ROC-AUC 0.86 and 90.9% recall on an imbalanced dataset (~5% bankruptcy rate)
Projected a 57.5% reduction in analyst workload on held-out test data by automatically clearing low-risk companies
Implemented MICE + RandomForest imputation for features with up to 39% missing values
Tuned XGBoost hyperparameters via Optuna (50 trials, F2-score objective); outperformed a Random Forest baseline on ROC-AUC,
recall, and PR-AUC at a matched decision threshold
Applied SHAP analysis confirming economic consistency of model decisions (profitability, liquidity, debt coverage)
EDUCATION
SGH Warsaw School of Economics
Bachelor in Quantitative Methods in Economics and Information Systems
Expected Graduation: 2027
GPA: 4.2/5.0
Relevant coursework:
Probability Theory, Econometrics, Induced Decision Rules (Machine Learning), Microeconomics, Computer
Programming, Accounting, Algebra, Optimization Methods, Statistics
Additional:
Mathematical Economics (Ordinary Differential Equations); independent study of real analysis (Zorich's
Mathematical Analysis), toward stochastic calculus
TECHNICAL SKILLS
Python: pandas, NumPy, scikit-learn, XGBoost, Optuna, SHAP, Matplotlib, SciPy, yfinance
SQL: joins, aggregation, nested subqueries
R: tidyverse (dplyr, ggplot2), decision trees, random forests
Microsoft Excel: data analysis, spreadsheet-based simulations
Power BI: data visualization, dashboards
LANGUAGES
English (C1)
Polish (B1)
Ukrainian (Native)
German (A1/A2 – Basic)
Russian (Native)
Daniil Hutsol Phone: [
Quantitative Methods Student, SGH Warsaw School of Economics Email: [
Location: Warsaw, Poland
SUMMARY
Quantitative Methods and Information Systems student who has spent the past year turning real-world data into working models, from
predicting corporate bankruptcy to optimizing investment portfolios.
RELEVANT EXPERIENCE & PROJECTS
Math Tutor | Private students & peer exam support, 2026
Tutored high school students in mathematics; supported university classmates preparing for Linear Algebra and Probability Theory
exams.
Portfolio Optimizer | Python, SciPy, yfinance
150+ ETF universe, 2020–2026, train/test split
Built a portfolio optimization pipeline combining Monte Carlo simulation with numerical optimization for a barbell asset allocation
strategy:
Simulated 300,000 candidate portfolios via Monte Carlo, then refined with SciPy SLSQP to maximize Sharpe ratio under barbell
allocation constraints (65–75% conservative, 25–35% high-yield), fitting weights on a training period and validating on a held-out test
period
Achieved an out-of-sample Sharpe Ratio of 1.09 (17.5% annualized return, 11.9% volatility, -10.1% max drawdown) on the test set,
a 9.7% degradation from the 1.21 in-sample Sharpe, indicating limited overfitting
Evaluated downside risk via Sortino, Calmar, and VaR/CVaR across an automated, 150+ ETF universe pulled and filtered via
yfinance
Bankruptcy Prediction System | Python, XGBoost, Optuna, SHAP
Polish Companies Dataset (2000–2013)
GitHub: github.com/lzanam1/bancrucy
Built an ML pipeline to predict corporate bankruptcy for bank credit risk screening:
Achieved ROC-AUC 0.86 and 90.9% recall on an imbalanced dataset (~5% bankruptcy rate)
Projected a 57.5% reduction in analyst workload on held-out test data by automatically clearing low-risk companies
Implemented MICE + RandomForest imputation for features with up to 39% missing values
Tuned XGBoost hyperparameters via Optuna (50 trials, F2-score objective); outperformed a Random Forest baseline on ROC-AUC,
recall, and PR-AUC at a matched decision threshold
Applied SHAP analysis confirming economic consistency of model decisions (profitability, liquidity, debt coverage)
EDUCATION
SGH Warsaw School of Economics
Bachelor in Quantitative Methods in Economics and Information Systems
Expected Graduation: 2027
GPA: 4.2/5.0
Relevant coursework:
Probability Theory, Econometrics, Induced Decision Rules (Machine Learning), Microeconomics, Computer
Programming, Accounting, Algebra, Optimization Methods, Statistics
Additional:
Mathematical Economics (Ordinary Differential Equations); independent study of real analysis (Zorich's
Mathematical Analysis), toward stochastic calculus
TECHNICAL SKILLS
Python: pandas, NumPy, scikit-learn, XGBoost, Optuna, SHAP, Matplotlib, SciPy, yfinance
SQL: joins, aggregation, nested subqueries
R: tidyverse (dplyr, ggplot2), decision trees, random forests
Microsoft Excel: data analysis, spreadsheet-based simulations
Power BI: data visualization, dashboards
LANGUAGES
English (C1)
Polish (B1)
Ukrainian (Native)
German (A1/A2 – Basic)
Russian (Native)
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The candidate's resume is posted at https://www.work.ua/resumes/18629721/