Aakash Gupta

Associate Consultant

Kolkata, West Bengal, India5 yrs 2 mos experience

Most Likely To SwitchAI Enabled

Key Highlights

Expert in Quantitative Finance and Risk Modeling.
Proven track record in predictive analytics and data-driven decision making.
Strong foundation in advanced statistical techniques and programming.

Stackforce AI infers this person is a Quantitative Analyst specializing in Fintech and Data Science.

Contact

Skills

Core Skills

Quantitative FinanceRisk ModelingData SciencePredictive Analytics

Other Skills

Advanced Multivariate AnalysisAlpha GenerationBollinger BandsC++Counterparty RiskData StructuresDecision TreesDeep LearningEconomic Factors AnalysisExploratory Data AnalysisFactor AnalysisFeature EngineeringFixed Income SecuritiesGradient BoostingGroup Theory

About

Experienced Quantitative Analyst at Morgan Stanley with a robust foundation in Statistics, Quantitative Finance, Stochastic Calculus, and Probability. Holds a Master’s degree in Statistics from the Indian Statistical Institute, Kolkata, with a deep understanding of data analysis. Passionate about using data-driven insights to guide strategic decision-making and enhance financial strategies. Committed to continuous learning and the application of advanced quantitative techniques to achieve measurable results in the evolving finance sector.

Experience

5 yrs 2 mos

Total Experience

1 yr 8 mos

Average Tenure

2 yrs 2 mos

Current Experience

Morgan stanley

Associate

Apr 2024 – Present · 2 yrs 2 mos · Mumbai, Maharashtra, India · On-site

Ubs

Authorised Officer (Quantitative Analyst)

Aug 2022 – Mar 2024 · 1 yr 7 mos · Mumbai, Maharashtra, India · On-site

Model Developer in Fixed Income portfolio
Counterparty Credit Risk Models

Credit suisse

Intern Quantitative Researcher

Feb 2022 – Aug 2022 · 6 mos · Mumbai Metropolitan Region · Hybrid

(Received PPO)
Quantifying Risks associated with Commercial Real Estate Loans Portfolio under Stress Scenarios
Study PD, LGD models - Python Implementation, streamlined Codes - reproducible across CCAR-DFAST Cycles
Exploring different techniques for CRE PD Modelling – Random Forests & Gradient Boosting
Analysing Wholesale PD Model – Impact of Economic Factors on Credit Rating downgrades
Explore Z-Factor Optimization using Maximum Likelihood Estimation - Enhancing Weighing Schemes used in Rating Transition Matrices

PythonRandom ForestsGradient BoostingEconomic Factors AnalysisQuantitative FinanceRisk Modeling

Anheuser-busch inbev

Intern Data Science Specialist

Aug 2021 – Oct 2021 · 2 mos · Bangalore Urban, Karnataka, India · Remote

(Received PPO)
Predictive Modeling of Beer Sales Estimate using Random Forrest. Segmentation of Unique Business.
Listing from Description of places of Consumption. - Increased Revenue by 30% by building library to automate the Google ID Mapping from Latitude-longitude values

Random ForestPredictive ModelingRevenue OptimizationData SciencePredictive Analytics