Apoorv Kumar Saxena

Business Analyst

Arlington, Virginia, United States6 yrs experience

Highly Stable

Key Highlights

Led a data extraction project processing 820 million records.
Developed a Python tool improving market risk analysis efficiency.
Achieved 95% accuracy in property classification using advanced techniques.

Stackforce AI infers this person is a Fintech professional with strong quantitative analysis and risk management expertise.

Contact

apoorvksaxena95@gmail.com LinkedIn

Skills

Core Skills

Natural Language ProcessingData AnalysisRisk ManagementCredit Risk ModelingFinancial Reporting

Other Skills

BankingBond MarketsCCNNsCapital MarketsCredit RiskDNNsData CleaningData ExtractionData GovernanceData MigrationDerivativesDerivatives TradingEquitiesEquity Trading

About

Quantitative and data analyst with 5 years of experience. CFA level-3 candidate with an adept understanding of Credit Risk, Market risk, Option Pricing, Bond Pricing, and Bond Pricing with option. Skilled in Financial, Business, Statistical, Risk and SDLC concept. Rich experience in Risk Processes, Business analysis, Requirement gathering, modeling, Credit Risk Reporting, Stakeholder management in an Agile environment.

Experience

6 yrs

Total Experience

4 yrs

Average Tenure

2 yrs

Current Experience

Freddie mac

2 roles

Senior Quantitative Analyst

Jun 2024 – Present · 2 yrs · McLean, Virginia, United States · On-site

Quantitative Intern – Modeling, Econometrics, Data Science & Analytics

May 2023 – Aug 2023 · 3 mos · Virginia, United States · Hybrid

I led a highly efficient data extraction project, processing 820 million property descriptions using multithreading and filtering for true positives and true negatives, resulting in a refined dataset of 200 million rows. Employing NLP techniques for data cleanliness, I reduced processing time from 4 hours to just 30 minutes for 100,000 rows using multiprocessing. I transformed text data into high-dimensional vectors with TF-IDF and Word2Vec, facilitating powerful analysis and classification of properties with Accessory Dwelling Units (ADUs). Leveraging XGB Classifier, CNNs, and DNNs, I achieved an 88% accuracy rate in ADU detection and further improved it to 95% through hyper-parameter optimization. This project significantly enhances data quality and analysis capabilities for future use.

Natural Language ProcessingData ExtractionMultithreadingData CleaningTF-IDFWord2Vec+4

Nyu tandon, finance & risk engineering department

Course Assistant

Sep 2022 – Jun 2023 · 9 mos · New York City Metropolitan Area

FRE-GY 6123 Financial Risk Management

Credit suisse

3 roles

Quantitative Analyst

Aug 2019 – Jul 2022 · 2 yrs 11 mos

Developed a Python tool for analyzing the gaps and impurities in Market Risk data increasing the efficiency of the team by 6 days per month per person by eradicating manual calculations
Python tool was used in formation of LDE, Dataset and Activity and bulk upload of these assets to Collibra
Used Python NumPy, pandas, SciPy libraries for market risk data analysis and delivering the data to modelling teams
The tool analyzes 10 parameters with 20,000 data points per domain for 6 Domains
Developed a UI using tkinter for the easy of usage by risk managers of all the Domains
Developed and maintained Data Flows for Market Risk in collibra as per Data Governance Rules
Defined the parameters for VaR, IRC models as per FRTB requirements
Represented the newly established risk measures in Governance forums to market risk managers and stakeholders

PythonData AnalysisMarket RiskData GovernanceUI DevelopmentRisk Management

Quantitative Analyst (Credit Risk)/ REPO

Jul 2018 – Aug 2019 · 1 yr 1 mo

Process improvement of the automated trade settlement process in portfolio management through secondary repo platforms. Made FRD and coordinated with developers for implementation.
Introduce the algorithm of the threshold repo interest rate for the REPO transaction containing the rehypothecation clause. Made a BRD and FRD for the whole algorithm and architectural implementation.
Establish a new legal entity and desk for REPO trade under the BREXIT project and introduced new established legal entity into trade booking and settlement algorithms.
Write SQL queries and publish reports of profit and loss on various books and customer IDs.
Made a linear interpolation model to model interest rate for bonds for various maturities and used the calculated interest rate for calculating exposures.
Made a credit Risk model and exposure calculation model of Repo trades for new security and bonds using the predicted interest rate of a newly issued security.
Demonstrated and explained the credit risk model and exposure calculation model to Global BA team and global traders
Coordinated with third-party vendor IHS MARKIT to migrate two repo index fields volume weighted average fee and benchmark fee into a portfolio management tool. Modeled the effect of these fields into the exposure calculation.

SQLProcess ImprovementCredit Risk ModelingFinancial ReportingData Migration

Summer Intern

May 2017 – Jul 2017 · 2 mos · Maharashtra, India

Calculated the parameters REPO rate, principal, coupon amount, rehypothecation process in REPO transactions and verified the calculation with the historic trades.
Document and present the calculation and trade booking process to various all the stakeholders in India.