Co-Founder at Stealth Startup (Fin + Ed Tech) (2025-11 – Present)
- Architected an AI-powered fintech and education platform integrating quantitative forecasting models, brokerage APIs, LLM powered investment assistants, and adaptive learning experiences to deliver personalized investing tools and educational content.
- Led end-to-end product strategy by defining the technical roadmap, developing financial and operating models, designing subscription pricing, evaluating market opportunities, customer acquisition strategy, and producing investor materials.
Analyst - Quantitative Research (RQA Group) at BlackRock (2024-08 – 2026-02)
- Built and extended investment systems across multi-asset strategies, emphasizing factor signal generation, robustness testing across market regimes, maximizing risk-adjusted return, optimizing diversification effects, and integrating into scalable research pipelines.
- Engineered scalable Python data pipelines and modular analytics systems on large financial datasets to enable factor exposure analysis, risk anomaly detection, and holdings decomposition models, utilizing reusable APIs for production integration.
- Developed backtesting and simulation frameworks using perturbation-based stress scenarios to evaluate portfolio P&L, factor risk contributions, hypothetical information ratios, and strategy positioning under varying macroeconomic catalysts.
- Built internal tools and dashboards (Power BI) for real-time monitoring and diagnostics of portfolio attribution, intraday/historical risk decomposition, and quantitative metrics, thereby improving reliability and transparency of investment workflows.
Summer Analyst in Risk and Quantitative Analysis Group at BlackRock (2023-06 – 2023-08)
- Developed data transformation pipelines using large-scale historical datasets to build quantitative analysis workflows for ETF and index portfolios, including futures optimization, cash management, optimal sampling, and tracking-error control versus benchmarks.
- Built a Python-based Risk Decomposition Dashboard to diagnose risk-range breaches and drill into factor, sector, and idiosyncratic risk contributions, incorporating real-time data ingestion, model debugging, feature engineering, interpolations, and visualizations.
- Developed classification models derived from Recurrent Neural Networks and stochastic optimization methods to automate risk-event categorization, reducing risk-resolution pipeline time by over 60% and increasing throughput for risk managers.
Software Engineering Intern at Reimagin: Business Intelligence Solution for ESG and Impact (2022-06 – 2022-08)
- Designed and optimized data pipelines aggregating ESG data from heterogeneous sources to enable unified and scalable analytics.
- Built natural language processing and large language models from unstructured company policy documents and news sources to extract structured signals like governance quality and environmental risk, and integrate them into scoring and screening workflows.
- Developed backend systems for data processing and retrieval to support scalable processes and enable data-driven decisions.
Data Engineering Intern at Pi Data Centers: India's First Uptime Institute & Tier 4 data center (2021-05 – 2021-07)
- Developed predictive models on operational and business metrics, identifying key drivers and anomalies in large time-series datasets, thereby improving data-driven decision-making and process efficiency by around 120%.
- Collaborated with stakeholders to translate model outputs into actionable improvements. Built automated reporting and alerting scripts in Python to monitor KPIs, data validation, and failure detection to improve system reliability and data-informed decisions.
