Nikhil Garg

Management Consultant

Jaipur, Rajasthan, India1 yr 9 mos experience

Key Highlights

Expert in Quantitative Finance and Risk Management.
Proficient in R and Python for data analysis.
Achieved significant returns using advanced asset pricing models.

Stackforce AI infers this person is a Quantitative Finance and Data Science professional with a strong analytical background.

Contact

Skills

Core Skills

Quantitative FinanceRisk ManagementData Science

Other Skills

Analytical SkillsArithmeticCC++Case StudiesData AnalysisEconometricsFinanceMachine LearningMarket ResearchMarket RiskMathematicsMicrosoft ExcelObject-Oriented Programming (OOP)Options

About

I have very keen interest in Quantitative Finance, Data Science, Maths and Programming. Apart from being well versed with basics of Risk Management, I have also sound knowledge about the Capital Asset Pricing Model, Investment and Portfolio Optimization (Modern Portfolio Theory), and Derivatives Pricing. I also love learning more about Finance and am currently exploring various fields in the same. I am also acquainted with Programming languages such as C, C++, Python and R and am interested in Data Science and Machine Learning. Playing Chess is one of my most prominent hobbies.

Experience

Squarepoint

Desk Quant Analyst

Jul 2025 – Present · 8 mos · Bengaluru, Karnataka, India · On-site

Part of CTA - Mortgages Team

Quantitative FinanceRisk Management

Goldman sachs

Summer Analyst

May 2024 – Jul 2024 · 2 mos · Bengaluru, Karnataka, India · On-site

Part of SMM Credit Strats Team.

Indian institute of management ahmedabad

Summer Research Intern

May 2022 – Jul 2022 · 2 mos · Ahmedabad, Gujarat, India

Worked on Extending the Fama-French Four Factor Asset Pricing Model in the Indian Equities Market from Jan 1993 till December 2021.
The dataset included around 8600 companies listed on BSE, around 18.5 million rows * 10 columns.
Employed advanced R packages such as data. table, dplyr, magrittr, lubridate, purrr, tidyverse, ggplot2 for Statistical Analysis and Four Factor Calculation.
Achieved Cumulative Four Factor Returns of 404% on WML (Winner Minus Loser), 129% MRP (Market Risk Premium), 55% on SMB (Small Minus Big), and 53% on HML (High Minus Low).

Statistical AnalysisR ProgrammingData AnalysisQuantitative FinanceData Science