Jan 2025 – May 2025 · 4 mos · New York City Metropolitan Area · On-site

• ● Graduate Teaching Assistant (TA) for Machine Learning (FRE-GY 7773), by Prof. Ken Perry at NYU

Oct 2024 – Jan 2025 · 3 mos · New York City Metropolitan Area · On-site

• ● Graduate Teaching Assistant (TA) for Advanced Deep Learning (FRE-GY 7871), by Prof. Ken Perry at NYU

Sep 2024 – Nov 2024 · 2 mos · New York City Metropolitan Area · On-site

• ● Graduate Teaching Assistant (TA) for NLP & The Investment Process (FRE-GY 7871), by Prof. Dan Rodriguez at NYU

Jun 2024 – May 2025 · 11 mos · New York City Metropolitan Area

• ● Conduct in-depth market analysis and financial due diligence to identify and evaluate high-potential startups in the deep tech sector, ensuring alignment with the strategic objectives of EFL’s accelerator program.
• ● Utilize Python and SQL to automate the extraction and processing of large datasets, significantly enhancing the accuracy and efficiency of decision-making processes.
• ● Collaborate with cross-functional teams to optimize internal databases, reducing data redundancy and improving the overall performance of financial models and reporting systems.
• ● Contribute to the development of strategic insights by continuously monitoring emerging technologies, industry trends, and competitive landscapes to inform the accelerator's investment decisions.
• ● Present detailed reports and recommendations to senior leadership, helping shape the selection and support strategies for startups within the program.

Dec 2022 – Aug 2023 · 8 mos · Gurugram, Haryana, India · On-site

• Played a crucial role in the development and implementation of investment strategies focusing on the equities markets of India and Hong Kong. My contributions included:
• ● Designing innovative signal protocols tailored for a long-only investment strategy, which was distinguished by a medium-term rebalancing cycle of 30 days. This approach optimized the portfolio's performance by capitalizing on the unique market dynamics of these regions.
• ● Managing a significant portfolio with assets under management (AUM) totaling $24.3 million. This role encompassed the entire investment process, from hypothesis formulation through to Python-based backtesting and the generation of monthly signals to guide investment decisions.
• ● Employing a data-driven approach to identify and exploit market inefficiencies, thereby enhancing portfolio returns while adhering to a disciplined risk management framework.
• ● Collaborating closely with a team of analysts and other researchers to continuously refine our investment models based on emerging market trends and data insights.
• This experience allowed me to significantly contribute to our investment team's success, leveraging my quantitative skills to develop and implement strategies that drove substantial portfolio growth.

Jul 2021 – Dec 2022 · 1 yr 5 mos · Gurugram, Haryana, India · On-site

• ● Designed and implemented a real-time risk engine capable of sub-millisecond recalibration, enabling dynamic exposure adjustments in volatile market conditions including tools for pre and post trade risk management, including limit setting, monitoring and alerting systems.
• ● Built an event-driven strategy builder and execution engine using Node.js, FastAPI, C++, and SQL, supporting the creation, simulation, and deployment of algorithmic trading strategies.
• ● Engineered low-latency trading middleware for high-frequency trading (HFT), achieving execution latencies as low as 5.xn--9s-99b for institutional traders.
• ● Led cross-functional integration by designing and maintaining REST APIs, WebSocket services, and backend communication through RabbitMQ, ensuring seamless collaboration between frontend and backend teams. tolerance, resulting in significantly processing throughput.

Aug 2020 – Jul 2021 · 11 mos · Gurugram, Haryana, India · On-site

• I actively contributed to the full-stack development of 'Gulaq', a Robo-Advisory platform. My work involved:
• ● Engineering the advisory engine with Python, enabling complex financial analyses and the creation of personalized investment strategies for our clients.
• ● Developing the backend infrastructure using Node.js, which was critical for the platform's seamless operation and scalability, allowing us to handle increasing user demand efficiently.
• ● Architecting the system's infrastructure on AWS, ensuring high availability and robustness to support our growing base of users and their data needs.
• ● Designing and managing the database architecture with SQL, optimizing for performance and reliability in handling and querying complex datasets.