Shivam Garg

Associate Consultant

Chicago, Illinois, United States2 yrs experience

Highly Stable

Key Highlights

Strong foundation in mathematical modeling and computational techniques.
Experience in validating quantitative models in finance.
Proficient in machine learning techniques for financial applications.

Stackforce AI infers this person is a Fintech Quantitative Researcher with expertise in machine learning and big data solutions.

Contact

mast.shivam007@gmail.com LinkedIn

Skills

Core Skills

Quantitative ResearchStatistical ModelingBig Data SolutionsKnowledge Graph

Other Skills

Alternative DataAnalytical SkillsC++FinanceHadoopInterpersonal SkillsJavaKDB+Machine LearningMapReduceMarket ResearchPythonResearch SkillsStatistical ResearchTime Series Forecasting

About

Graduate student in the Financial Mathematics at the University of Chicago, set to graduate in December 2024. With a strong foundation in mathematical modeling and computational techniques, I am eager to apply these skills to solve complex problems in the finance industry. Actively seeking quantitative research & development opportunities.

Experience

Barclays investment bank

Quantitative Research Associate Intern

Jun 2024 – Aug 2024 · 2 mos · New York, United States · On-site

Credit Systematic Market Making
Researched on Corporate Bond Liquidity: Validated a model which utilizes past volume, bond characteristic features combining into a score; compared it against MarketAxess Liquidity Scores on TRACE, and other extensive bond volume datasets fetched from KDB+; Productionized the liquidity score validation framework using Python
Improved the model by 3% by enhancing one of its key features, leveraging insights from Machine Learning techniques Elastic Net, Random Forest, and XGBoost for feature selection and identifying non-linear relationships

PythonMachine LearningKDB+Quantitative ResearchStatistical Modeling

Numeraxial llc

Quantitative Researcher - University of Chicago Project Lab

Mar 2024 – May 2024 · 2 mos · Chicago, Illinois, United States

Alpharoc

Quantitative Researcher – University of Chicago Project Lab

Oct 2023 – Dec 2023 · 2 mos · Chicago, Illinois, United States

Designed a machine learning framework integrating aggregation, smoothing, and rigorous testing pipelines to predict macroeconomic indicators (GDP, inflation, consumer index), leveraging survey data from AlphaROC's Occam

Goldman sachs

Full Time Analyst

Jul 2021 – Jul 2023 · 2 yrs · Bengaluru, Karnataka, India · On-site

Knowledge Graph Team
Designed and Maintained big data solutions on top of peta-scale, firm-wide data modeled into a graph with vertices (entities) and edges (relationships) for applications to banking activities like compliance and investment banking
Developed the infrastructure for a knowledge graph deduplication system from scratch in Hadoop MapReduce, sourcing data from multiple sources (60 million vertices, 4 billion edges), leading to duplication of nodes in the graph
Designed an algorithm using the longest common subsequence to identify the company domain and then the best person in graph for making introductions to potential clients of IBD; Integrated using REST API, Spring in Java

HadoopJavaMapReduceBig Data SolutionsKnowledge Graph

Estee advisors private limited

Quantitative Researcher

Jun 2020 – Jul 2020 · 1 mo · Gurugram, Haryana, India

Research in Financial Markets on Low Frequency OHLCV data
Designed and implemented alpha-generating signals in C++ for optimizing weight allocation across a portfolio of 130 Indian stocks, utilizing Technical Indicators on OHLCV daily frequency data, Sharpe ratio for performance evaluation
Leveraged Time Series forecasting techniques ARCH, and GARCH to estimate volatility as a risk measure in alphas
Conducted comparative analysis of Deep Learning models (RNN, LSTM, CNN) for predicting stock prices

C++Time Series ForecastingQuantitative Research