Shashank Suresh Rotti ad473r@r.postjobfree.com • 347-***-****

LinkedIn • Portfolio • GitHub

SUMMARY

Enthusiastic individual with a strong foundation in quantitative analysis, programming, and financial modelling; well-prepared to excel in a quantitative role by leveraging a diverse skill set and avid interest. Demonstrated ability to apply statistical techniques to mine data for valuable insights. Adept at extracting insights from large datasets to inform research and decision-making processes. WORK HISTORY

Trade Terminal, Campbell, CA

Quantitative Researcher Intern Jan 2024 - Present

● Developed and executed a market making strategy from concept to completion, enhancing liquidity and trading efficiency, focused on high-

● frequency Conducted trades. research This on resulted lead-lag in arbitrage a portfolio to improve activity ratio the hit of rate 56 and of cross-a 2% profit exchange increase market in one making month. strategy by 5%, thereby leading to better

● spread Developed capture Python and scripts enhanced for data profitability. aggregation and processing from multiple time-series databases, enabling real-time risk metrics visualization Bloomberg, on Grafana, New enhancing York, NY portfolio risk management efficiency by 50%. Quantitative Researcher - Industry Capstone: Thematic Investing using NLP May 2023 - Aug 2023

● ● Developed Calculated company NLP methodologies exposure scores for theme based detection, on supply using chain techniques connections, such attaining as Semantic 94% Embedding precision in and theme text specification. classification. Deloitte, Analyst ● Designed Consultant Hyderabad, and implemented - India Core Business an end-Operations to-end data model, developed Python ETL scripts for migrating claims data from legacy Aug 2018 RDBMS - May to 2022 cloud data warehouse, enhancing downstream analysis and saving $65M annually.

● Built a customized data visualization tool using Python and Tableau to integrate data from multiple vendors' data science infrastructure, enabling validation, data profiling, and achieving a 30% reduction in processing time.

● Worked in an agile software development environment to identify and rectify critical payment flow functionality issues for a client, saving

$50M in annual revenue leakage.

EDUCATION

Master of Science in Financial Engineering; GPA: 3.9/4.0 - New York University, Brooklyn, NY, Expected May 2024 Bachelor of Engineering in Computer Science; GPA: 8.9/10.0 - PES Institute of Technology, Bengaluru, India, Jun 2018 ACADEMIC PROJECTS

New York University, Brooklyn, NY

Quantitative Risk Analysis and Portfolio Management, Finance & Risk Engineering Department

● Implemented diverse VaR calculation methods including Monte Carlo simulations, variance-covariance, and historical simulation augmenting risk metrics to guide capital reserve adequacy under CCAR and DFAST mandates.

● Executed scenario-based stress tests, leveraging economic data analytics, to evaluate portfolio resilience against adverse conditions and mitigate the impacts of market downturns on portfolio performance. Strategic Portfolio Optimization and Risk Mitigation, Finance & Risk Engineering Department

● Implemented a dynamic momentum strategy based on volatility forecasting, mitigating crash risk, and reducing portfolio drawdown to -4.9% versus benchmark DJTMNMO’s -20.97%.

● Enhanced portfolio efficiency with a 60% increase in the Sharpe ratio through market variance analysis and bear market signals. Option Pricing using Numerical Methods, Finance & Risk Engineering Department

● Applied Finite Differences, Monte Carlo simulation, and OOP concepts in C++ to price European, American, and Asian options.

● Minimized errors in determining price, delta, and gamma of the derivatives by implementing control variate technique. Modelling and Valuation of Collateralized Debt Obligation (CDO), Finance & Risk Engineering Department

● Modeled cash flows for a correlated corporate bond portfolio using a waterfall structure with PAC, Mezzanine, and equity tranches.

● Determined optimal notional values and discounted expected returns, assessing the impact of leverage on return profiles and default probabilities.

Credit and Bankruptcy Analysis, Finance & Risk Engineering Department

● Performed exploratory data analysis, feature engineering (PCA and Mutual Information) and applied ML models for bankruptcy analysis.

● Conducted error analysis and concluded that XGBoost with 89% recall and 96% accuracy outperformed SVM and Random Forest models. TECHNICAL SKILLS AND CERTIFICATIONS

Skills: Python C++ SQL Linux MATLAB Git Pandas TensorFlow Scikit Learn MS Excel VBA MS Word MS PowerPoint Certifications: CFA Level 1 AWS Certified Developer - Associate Bloomberg Markets Certification

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