KYRIAKOS MAVRIDIS

Results-driven Data Scientist with a strong business and strategy background. Adept at aligning data-driven initiatives with organizational goals. Skilled in developing minimum viable products (MVPs), optimizing infrastructure-constrained code, and applying statistical experimentation for decision-making. Extensive experience in managing cross-functional teams for machine learning prototyping and product development in software-as-a-service (SaaS) environments. Drives scalable solutions from concept to production. Deep expertise in quantitative finance, treasury operations, hedge fund strategies, and risk management . Proficient in pricing algorithms , including NYOP models and risk-based pricing for insurance products. Possesses a strong command of Value at Risk (VaR) calculations, risk frameworks, and comprehensive risk reporting methodologies.

Optimized Data Processing for ML Training: Developed a high-efficiency data filtering function, reducing billions of records while maintaining representativeness, significantly accelerating ML pricing model training. This innovation streamlined data processing, reducing computational overhead and enabling faster model deployment in production.

Constraint-Optimized Pricing Model: Designed a mathematically optimized pricing framework using Lagrange multipliers, ensuring all business constraints (e.g., demand elasticity, supply availability, and revenue targets) were systematically incorporated. This approach provided a closed-form solution, enhancing model interpretability and improving pricing precision under real-world constraints.

Profit Growth Boost

Increased company revenue by 20% by developing strategic pricing models leveraging ML-driven almost real-time market adjustments

Experimentation Framework Leadership

Successful testing frameworks A/B and Multi Arm with focus on pricing.

Risk Management Mitigation: FX Hedging

Managed FX risk exposure across 35 currencies, implementing a hybrid hedging strategy that combined natural operational hedges with financial derivatives, reducing currency volatility impact on profitability.

Operational Risk: Fraud

Reduced fraudulent transactions by more than 20% via fraud prevention machine learning algorithms, incorporating real-time anomaly detection and risk-scoring mechanisms to safeguard transactions in a high-risk OTA environment.