Finance & Quant Analytics — MS Finance (Quantitative Mathematics), University at Buffalo ’25 · MBA (Finance & HR), Anna University ’22 · M.Com (Finance & Accounting), Annamalai University ’22

Python · R · SQL · Excel (Advanced/VBA) · Power BI (DAX/Power Query) · Streamlit · LSEG Yield Book · NumPy · Pandas · Matplotlib · scikit-learn · Git/GitHub · Jupyter · Bloomberg Market Concepts (BMC)

Primary

• Website: https://aurokrishnaa.me
• Résumé: https://aurokrishnaa.me/resume.html
• Email: aurokrishnaa2000@gmail.com
• LinkedIn: https://www.linkedin.com/in/aurokrishnaa/

GitHub & Apps

• GitHub: https://github.com/Aurokrishnaa
• Black-Scholes Live App: https://black-scholes-aurokrishnaa.streamlit.app/
• Blog: https://www.auronomics.com
• Credit Risk Intelligence (Repo): https://github.com/Aurokrishnaa/credit-risk-intel

Research Profiles

• Google Scholar: https://scholar.google.com/citations?user=z6HyNn8AAAAJ&hl=en
• ORCID: https://orcid.org/0009-0001-4529-5143
• SSRN: (coming soon)

• Quantitative Finance Research — asset pricing, derivatives, financial econometrics, market microstructure
• Risk & Credit Analytics — PD/LGD/EAD, CECL/IFRS, portfolio stress testing, spreads & pricing
• Derivatives — Black-Scholes, Greeks, hedging, P&L/scenario analysis
• Fixed Income — duration/convexity, KR-DV01 ladders, curve shocks (Yield Book)
• ML/NLP in Finance — earnings-call sentiment, temporal validation, LLM-based document Q&A
• Delivery — explainable analytics, parameterized scenarios, executive-ready reports

Programming & Data

• Python (NumPy, Pandas, Matplotlib, scikit-learn), R (tidyverse), SQL
• Feature engineering · backtesting · Monte Carlo simulation · time series · data validation

Analytics & BI

• Excel (advanced modeling, VBA) · Power BI (DAX, Power Query) · Streamlit apps
• Decision dashboards · analytical web tools · reporting pipelines

Risk, Fixed Income & Valuation

• LSEG Yield Book: duration/convexity · KR-DV01 ladders · hedging illustrations
• CECL/IFRS rollups · stress testing frameworks · pricing heuristics

Derivatives

• Black-Scholes & Greeks · scenario/P&L analysis · risk-neutral valuation

ML/NLP in Finance

• Earnings-call sentiment classification · temporal ML setup · document Q&A (LLM pipelines)

Engineering Workflow

• Git/GitHub · parameterized runs · reproducible experiments · documented assumptions

• Credit Risk Intelligence — portfolio stress tests, explainable scores, CECL/IFRS aggregates, pricing, executive PDF.
• Black-Scholes Dashboard (Streamlit) — valuation, Greeks, scenarios, P&L; live web app.
• Fixed-Income Analytics (Yield Book) — curve shifts, KR-DV01 ladders, simple hedges.
• Earnings-Call NLP — tone/sentiment signals; temporal ML setup; document Q&A prototype.

• MS Finance (Quantitative Mathematics) — University at Buffalo, SUNY (’25)
• MBA (Finance & HR) — Anna University (’22)
• M.Com (Finance & Accounting) — Annamalai University (’22)
• B.Com (Finance & Accounting) — University of Madras (’20)
• CA Foundation & Articleship — The Institute of Chartered Accountants of India (ICAI)

Exploring Finance / Quantitative Analyst / Research roles where rigorous modeling and data-driven methods meet clear business decisions. Long-term interest in advancing to PhD-level research in Finance focused on quantitative methods, derivatives, and computational finance.