Кирилo Мосiйчук

Quantitative Developer with nearly four years of experience building market-risk pipelines and derivatives analytics for international banks. Combines production Python data engineering with stochastic modelling, Monte Carlo simulation, PDE methods, and machine-learning-based time-series forecasting. Research background in stochastic control, option dynamics, and first-passage models for quantitative finance.

Middle Quantitative Developer at Compatibl (2025-02 – Present)

• Developed and maintained AutoSys-orchestrated ETL pipelines for market risk at a major U.S. bank, processing Parquet feeds with Dask and Polars to produce scenario-specific outputs controlled by configurations stored in Oracle Database. Produced curated Parquet datasets for traders and analytics teams, publishing them to Apache Pinot for low-latency analysis and distributing downstream outputs to Amazon S3 and Apache Hive.
• Migrated several processing jobs from pandas to Polars LazyFrames, improving execution speed and reducing memory consumption through lazy evaluation and query optimization. Contributed approximately 40% of the unit-test suite that helped the team raise code coverage to 90%.
• Implemented a Monte Carlo TARF pricing engine for a major German bank by extending the Compatibl.NET analytics platform with stochastic path simulation and quantile-based exposure modeling. Validated results against market benchmarks to meet client model-risk standards.
• Built a Python wrapper for an international bank's quantitative analytics API as part of an Excel-to-Python migration. Added a multiprocessing pytest suite to validate analytical assumptions and deterministic simulation results, reducing test runtime by more than 60%.
• Helped integrate large language models into analytical products for institutional clients, automating report generation and insights and reducing document-review time by 50–70%.

Junior Quantitative Developer at Compatibl (2022-10 – 2025-02)

• Developed and maintained AutoSys-orchestrated ETL pipelines for market risk at a major U.S. bank, processing Parquet feeds with Dask and Polars to produce scenario-specific outputs controlled by configurations stored in Oracle Database. Produced curated Parquet datasets for traders and analytics teams, publishing them to Apache Pinot for low-latency analysis and distributing downstream outputs to Amazon S3 and Apache Hive.
• Migrated several processing jobs from pandas to Polars LazyFrames, improving execution speed and reducing memory consumption through lazy evaluation and query optimization. Contributed approximately 40% of the unit-test suite that helped the team raise code coverage to 90%.
• Implemented a Monte Carlo TARF pricing engine for a major German bank by extending the Compatibl.NET analytics platform with stochastic path simulation and quantile-based exposure modeling. Validated results against market benchmarks to meet client model-risk standards.
• Built a Python wrapper for an international bank's quantitative analytics API as part of an Excel-to-Python migration. Added a multiprocessing pytest suite to validate analytical assumptions and deterministic simulation results, reducing test runtime by more than 60%.
• Helped integrate large language models into analytical products for institutional clients, automating report generation and insights and reducing document-review time by 50–70%.

Master's Degree in Mathematical Engineering – University of L'Aquila and Taras Shevchenko National University of Kyiv (2024-09 – 2026-07)

Bachelor of Science in Applied Mathematics – Taras Shevchenko National University of Kyiv (2020-09 – 2024-07)