Abhinav Sharma

Product Engineer

Delhi, India1 yr 5 mos experience

Most Likely To Switch

Key Highlights

Strong performance in Optiver mental-math challenge
Selected for Amazon’s ML Summer School from 125,000+ applicants
Consistent problem-solving across Brainstellar and Puzzled Quant

Stackforce AI infers this person is a Quantitative Analyst with expertise in financial markets and machine learning.

Contact

Skills

Core Skills

Quantitative AnalysisMarket MicrostructureQuantitative ResearchTrading StrategiesBacktestingResearchData AnalysisMachine LearningTime-series Analysis

Other Skills

ADFBrain APIC++Data ScienceDeep LearningEffective Transfer EntropyFIX/ETI log parsingFinancial DerivativesLinuxPCAPython (Programming Language)T-testsanomaly detectionbacktesting frameworksbacktesting platform

About

Hello! I’m Abhinav Sharma, pursuing a B.Tech in Energy Engineering at IIT Guwahati. During college, I developed a strong interest in quantitative finance, machine learning, and statistical methods for financial markets.During my internship at @iRage as a Quant Analyst, I’ve strengthened my skills in market microstructure, options Greeks and volatility modeling, execution/latency analytics, market making/taking strategies, FIX/ETI log parsing, and building reproducible data pipelines and backtesting frameworks that move research into production. Previously at WorldQuant, I developed and evaluated trading signals; at Rockwell Automation, I built time-series prediction models; and at HKKR Global, I created an end-to-end backtesting platform for trading strategies. Selected achievements: strong performance in the Optiver mental-math challenge; selected for Amazon’s ML Summer School from 125,000+ applicants; and consistent problem-solving across Brainstellar and Puzzled Quant. Open to conversations on trading research, ML for markets, and data infrastructure. Skills: Python, C++ (STL), pandas/numpy, PyTorch, statistics, optimization, time-series modeling, options/Greeks, backtesting, Linux. Contact: abhinavkdsh17@gmail.com | +91-76691-89417

Experience

Irage

Quantitative Analyst

May 2025 – Present · 10 mos · Mumbai Metropolitan Region · On-site

At iRage, I’ve strengthened my skills in market microstructure, options Greeks and volatility modeling, execution/latency analytics, FIX/ETI log parsing, market making /taking strategies, and building reproducible data pipelines and backtesting frameworks that move research into production.

market microstructureoptions Greeksvolatility modelingexecution analyticsFIX/ETI log parsingdata pipelines+3

Hkkr global

Quantitative Analyst

Nov 2024 – Jan 2025 · 2 mos · Delhi, India

◦Developed a universal backtesting platform for options trading across various markets, by tracking segment & index.
◦Effectively managed options expiry accommodating various expiry types (daily,weekly,monthly) & days to expiry (DTE).
◦Created the LEG function for executing complex multi-leg options strategies, enhancing market timing and profitability.

backtesting platformoptions tradingmulti-leg options strategiesQuantitative AnalysisBacktesting

Worldquant

Quantitative Researcher

Aug 2024 – Mar 2025 · 7 mos · Remote

◦Developing alphas (trading algorithms) for long-short market-neutral strategies in US and China markets.
◦Utilizing Brain API for optimization and created strategies with high Sharpe ratios, low risk & high returns.
◦Employing PCA, ADF and T-tests to validate the hypotheses underlying the trading strategies.

trading algorithmsBrain APIPCAADFT-testsQuantitative Research+1

Xlri jamshedpur

Research Intern

Dec 2023 – Feb 2024 · 2 mos · Remote

◦Applied advanced probability & information theory techniques to analyze financial time series data, assessing expected values & surpriseness of events through histogram approach in both discrete & continuous process.
◦Implemented Effective Transfer Entropy on stock pairs, contributing to the understanding of information flow patterns in financial markets. Explored foundational concepts of entropy, mutual information & randomness.

probability theoryinformation theoryfinancial time series analysisEffective Transfer EntropyResearchData Analysis

Rockwell automation

Machine Learning Engineer

Dec 2023 – Jan 2024 · 1 mo · Gurugram, Haryana, India · On-site

◦Developed a robust time-series model predicting sales by Auto Regressive, Moving Average & Deep Learning Models.
◦Applied feature engineering to boost predictive power, implemented multiple ML models, deploying the best model.
◦Predicted Anomalies in machine operations well before occurrence ensuring anomaly detection by soft sensors.

time-series modelingfeature engineeringanomaly detectionDeep LearningMachine LearningTime-Series Analysis