Anuj Choudhury

Product Manager

New York, New York, United States6 yrs 7 mos experience

Highly Stable

Key Highlights

Expert in transforming complex algorithms into production-level code.
Achieved a Sharpe ratio of 2.4 in quantitative strategies.
Strong background in quantitative research and machine learning.

Stackforce AI infers this person is a Quantitative Researcher with expertise in Fintech and advanced analytics.

Contact

Skills

Core Skills

Quantitative ResearchMachine LearningRisk Measurement

Other Skills

AlgorithmsAlpha GenerationAnalytical SkillsC (Programming Language)C++Data ScienceData StructuresDeep LearningFinanceHigh Performance Computing (HPC)Machine Learning AlgorithmsMathematical StatisticsMathematicsNatural Language Processing (NLP)Portfolio Construction

About

Quant with solid research skills in mathematics, statistics and algorithms. Strong problem solving skills. Experienced in transforming complicated academic ideas and algorithms into effectively working production level code. Have previously worked as a developer in a HFT firm, working on production level C++ code for multiple exchanges. Graduated with a major in Computer Science with an all round understanding of diverse fields in CS. Actively looking for Quant roles where I can contribute to a dynamic team, I can be reached out at anuj.choudhury@berkeley.edu

Experience

Goldman sachs

2 roles

Quantitative Strategist

Jun 2025 – Present · 9 mos · New York City Metropolitan Area · On-site

Quantitative Strategist in the Systematic Market Making Team for Interest Rate Products in GBM (Global Banking and Markets)

Quantitative Strategist

May 2024 – May 2025 · 1 yr · New York City Metropolitan Area · On-site

Quantitative Strategist in the External Investment Group (XIG Strats), working in the Private Equity and Credit space at Goldman Sachs Asset Management (GSAM) division

Panagora asset management

Quantitative Researcher

Oct 2023 – Jan 2024 · 3 mos · Boston, Massachusetts, United States · On-site

Developed systematic alpha signals from earnings call transcripts using NLP techniques, employing Machine Learning and deep learning methodologies for accurate stock selection and sentiment analysis.
Implemented long-only and long-short strategies, achieving a notable Sharpe ratio of 2.4. Managed liquidity and sector constraints and executed effective factor neutralizations to optimise performance.

Natural Language Processing (NLP)Machine LearningDeep LearningStatistical TechniquesSentiment AnalysisQuantitative Research

Temasek

Quantitative Researcher

Aug 2023 – Oct 2023 · 2 mos

Performing alpha research using proprietary employment data and MSCI stock factors to pinpoint potential long-short trading prospects within the Chinese markets.
Utilizing statistical and regression techniques to extract actionable insights and construct a balanced portfolio that maximizes returns while minimizing risks.

Statistical TechniquesRegression TechniquesPortfolio ConstructionQuantitative ResearchRisk Measurement