Hi, my name is
Tanishk Yadav.
I am an MS Financial Engineering candidate with a passion for building robust financial models and data-driven trading strategies. My focus is on quantitative analysis, risk management, and leveraging technology to solve complex financial problems.
About Me
Hello! I'm Tanishk Yadav, a Master of Financial Engineering student at New York University, graduating in May 2027. My academic journey began with a B.Tech in Computer Science, which provided me with a strong foundation in software development and algorithmic problem-solving.
My core interest lies at the intersection of finance and technology. I am passionate about developing quantitative strategies, analyzing market dynamics, and managing risk. Through my projects and coursework, I have gained hands-on experience with derivatives pricing, portfolio theory, and machine learning models applied to financial data.
I am currently seeking a Summer 2026 internship in quantitative finance, trading, or risk management where I can apply my technical skills and analytical mindset to make a tangible impact.
Professional Experience
Founder
Apr 2021 - Jan 2025
- Delivered expert instruction in Quantitative Aptitude and Logical Reasoning, growing the channel to 1,800+ subscribers and 200K+ views.
- Collaborated with educators to research and create engaging video content for 10+ competitive exams.
Online Associate AI & ML Tutor
@ ULearn - EDU
Sep 2024 - Jan 2025
- Taught and mentored 50 undergraduate students in AI & ML concepts over 120 hours of virtual instruction.
- Provided personalized guidance on 500+ coding problems and student research projects.
Framework Development Intern
Jun 2023 - Aug 2024
- Contributed to a data exchange framework using cryptography and web development to create secure, digitally signed artifacts for public sector companies.
Technical Skills
Programming Languages
- Python
- C++
- SQL
- Java
- R
- MATLAB
Libraries & Tools
- Pandas
- NumPy
- Scikit-learn
- TensorFlow
- PyTorch
- GitHub
Financial Concepts
- Derivatives Pricing
- Portfolio Theory
- Risk Metrics (VaR, CVaR)
- Algorithmic Trading
- Monte Carlo Simulation
- Black-Scholes Model
Featured Projects
S&P 500 Sector Analysis & Clustering
Analyzed S&P 500 firms to identify monopolistic players and used clustering algorithms to find cohesive movement between correlated sectors.
- Python
- Pandas
- Scikit-learn
- Clustering
Stock Price Prediction with LSTM
Developed a stock price forecasting system using Random Forest and LSTM models, enhanced by technical indicators like RSI, MACD, and Bollinger Bands.
- Python
- TensorFlow
- LSTM
- Random Forest
