Data Scientist

Automation validation processes using LLM. Led development of an LLM evaluation library reducing development and testing time by 60%, accelerating product releases. Deployed LLM-as-judge solutions saving up to 50% of human labor costs. Integrated RAGs, chatbots in models validation process reducing validation time by 35%. Researched and implemented advanced LLM solutions including agents and PEFT fine-tuned models using LoRA. Worked with cross-functional teams to implement AI technologies.

Developed ML and pricing models in trading book. Designed and implemented a testing library for pricing models accelerating product releases by 50%. Identified and resolved critical gaps in risk metrics for exotic products preventing significant financial losses. Developed predictive and regression models for traders and analysts increasing trading accuracy and profitability. Improved strategic risk-adjusted trading decisions and automated hedging error calculation.

Financial sector risk assessment. Developed adaptive ML pipelines for crypto token prediction dynamically selecting CatBoost, RandomForest, and LSTM models improving ROC-AUC by 7%. Optimized classification and ranking models for token risk assessment increasing ROC-AUC and F1 scores by 7%. Built automated alerting and retraining system to detect model degradation reducing manual adjustments by 40%. Enhanced backtesting framework for crypto forecasting models improving predictive stability by 5%. Collaborated with product and marketing teams to align ML outputs with business goals contributing to 18% LTV growth and 15% higher customer satisfaction.