Alexander Alimov

WORK HISTORY

EDUCATION

ABOUT ALEXANDER ALIMOV

Quantitative Analyst and Researcher with 5+ years of experience developing sophisticated financial models and alternative data strategies for institutional clients. Deep expertise in Monte Carlo simulations, regulatory capital models, and financial data integration across multiple asset classes.Proven track record of building production-ready quantitative models, integrating alternative data and developing systematic approaches to risk assessment and management. Expert in Python-based quantitative research, cloud-scale data processing, and translating complex statistical models into actionable business insights for financial institutions and portfolio managers.Selected ExperienceAssociate | Oliver Wyman | London (2022 - Present)• Developed systematic risk models for multi-asset portfolios - designed and implemented Monte Carlo simulation frameworks for cash flow, interest rate, and FX risk modeling across project finance and renewable energy investments, validating performance against industry benchmarks and derivatives pricing models• Constructed regulatory capital models - developed production-ready CRD-IV credit risk models for Tier-1 bank, implementing advanced statistical methods for probability of default estimation, loss given default modeling, and economic capital allocation under Basel framework• Designed and implemented simulation-based risk models for project finance and renewable energy portfolios across multiple European banks, covering capital strategy, methodology, data, tooling, and regulatory interactionData Scienist | Oliver Wyman | Moscow (20••••21)• Architected systematic trading infrastructure - built end-to-end quantitative research platform processing real-time market data, alternative datasets, and fundamental information for systematic strategy development in SME credit and insurance marketsData Scienist | PricewaterhouseCoopers | Moscow (20••••20)• Created quantitative forecasting systems - engineered machine learning models for supply chain risk prediction using statistical analysis and pattern recognition, preventing $8M in operational losses through systematic early warning indicators and quantitative risk metrics