Niranjan Verma

AI/ML Engineer & Quantitative Analyst

LinkedIn | GitHub

About

Highly motivated and results-driven Chemical Engineering student with a strong passion for Artificial Intelligence, Machine Learning, and Quantitative Finance. Proven ability to develop and deploy advanced AI/ML models, conduct in-depth data analysis, and apply quantitative methods to solve complex problems in both technical and financial domains. Eager to leverage interdisciplinary expertise to drive innovation and achieve impactful results.

Work Experience

Investment Analyst Intern

HDFC Securities

Dec 2025 - Present

Contributed to quantitative financial analysis and portfolio optimization for a leading securities firm, leveraging data-driven insights to inform investment strategies.

  • Conducted in-depth quantitative analysis of over 50 auto-ancillary companies, leveraging a multi-factor stock model to identify investment opportunities.
  • Extracted and analyzed extensive historical financial data from the Capitaline Database to evaluate company performance and inform investment decisions.
  • Developed a Python-based algorithm to identify top and bottom-performing stocks by analyzing historical market data.
  • Successfully backtested dynamic and static investment portfolios over a 7-year period, optimizing portfolio weights to adapt to varying market conditions.

Education

Chemical Engineering

Indian Institute of Technology, Bombay

8.1 CGPA

Volunteer

Volunteer

National Service Scheme (NSS), IITB

Dedicated over 80 hours to community service initiatives, fostering social responsibility and contributing to various welfare programs.

  • Contributed over 80 hours to social service initiatives under the National Service Scheme (NSS) at IITB.

Projects

Image Deblurring Model

A course project involving the development of a deep learning model to enhance image clarity by removing blur.

Hemoglobin Level Estimation

An R&D project focused on predicting hemoglobin levels from eye conjunctiva images using advanced machine learning techniques.

Speech Emotion Recognition

Developed an NLP-based sequence model for real-time emotion detection from speech, leveraging extensive audio datasets.

Webpage Query Solver

A self-initiated project implementing a Retrieval-Augmented Generation (RAG) system for efficient webpage querying.

Facial Recognition System

Developed a robust facial recognition system as part of a Winter in Data Science program, focusing on facial similarity.

Awards

3rd Place in Optimizer Competition

AZeotropy

Secured 3rd place among 30 competing teams in the prestigious Optimizer competition, organized by AZeotropy, showcasing strong problem-solving and technical skills.

Skills

Programming Languages

  • Python
  • C++
  • MATLAB
  • JavaScript

Machine Learning & Deep Learning

  • TensorFlow
  • PyTorch
  • Keras
  • Scikit-learn
  • YOLOv5x
  • ResNet-50
  • LSTM
  • CNN
  • NLP
  • RAG

Data Analysis & Visualization

  • NumPy
  • Pandas
  • Matplotlib
  • OpenCV
  • Capitaline Database
  • VectorStoreIndex

Quantitative Finance

  • Multi-factor Models
  • Portfolio Optimization
  • Backtesting
  • Financial Modeling

Interests

Aeromodelling

  • RC Plane-making
  • Team Collaboration

Athletics

  • Cross Country
  • Institute-wide Competition