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Head Of Sector Analytics Resume

New York, NY

SOFTWARE TOOLS:

Expertise: Default Modeling, Consolidation Student Loans Interest Rate setting using Machine Learning, Scenario Framework, Credit Card Receivable Financing, Student Loans Financing, GSE Risk Sharing Mortgage Insurance, RPL, Risk Retention, SFR, COOF, NNN, FarmerMac, Corporate/Equity Screeners, CMBS Retail, Forbearance Recovery, Refi Eligibility, ROE calculator, CMBS Retail, Class B/C Malls, Investment Risk Budget, Consumer Unsecured Loan Financing, Non - QM Mortgage Financing, CMBS/RMBS/CLO/ABS asset database, CMBS Tenant / CLO Issuer matching

Programming/Software: Python, Flask, C#, C++, VBA, SQL, Slang, SecDb, 1010Data, CoreLogic, Intex Calc & Wrapper API, Yieldbook, CprCdr, Bloomberg, Sybase IQ, Blackrock Alaaddin/AnSer, Visible Alpha, Alpha-Sense

Machine Learning: Supervised Learning using XGBoost, GBM, SVM.

EXPERIENCE:

Confidential, New York, NY

Head of Sector Analytics

Responsibilities:

  • Analyzed deals by building cashflow/pricing models which includes private student loan($150m), credit cards ($200m).
  • Worked on GSE Risk sharing mortgage risk insurance deals and helped write insurance on $400m of risk in force. Worked on an excess of loss Australian Mortgage Insurance deal of size $500m.
  • Build various systems for risk management and surveillance of non - agency residential and commercial mortgages, CLOs, Student Loans ABS, other esoteric ABS (COOFs, SFR, CLO/CMBS Risk Retention). Total assets under coverage is around $20bn.
  • Developed scenario framework to run cashflows on residential mortgage & private student loans.
  • Built machine learning models for LendingClub and Prosper loans on default risk.
  • Worked on estimating NAIC capital and S&P Capital for insurance assets and built an REO calculator based on NAIC capital.
  • Built machine learning models for setting interest rate on student loan origination.
  • Matched CMBS Tenants with IG and High-Yield debt issuers, identified IG/Non-IG tenants and analyzed retail risk using store closures, Mall ratings etc.
  • Built a framework to qualify loans in CMBS holdings to watchlist status by running quantitative tests and systematically put bonds into watchlist.
  • Built screeners using layered risks, market data change, risk exposures like geo or disruption.

Confidential, New York, NY

Research Analyst

Responsibilities:

  • Built MSR pricing framework by creating replines and running through Yieldbook.
  • Developed surveillance for agency and non-agency mortgages using 1010Data and Python.
  • Built valuation and risk framework for agency pools using Yieldbook.

Confidential, New York, NY

Research Analyst

Responsibilities:

  • Improved bond valuation framework written in Python by analyzing servicers’ behavior, collateral performance, timelines, roll rate and severities etc.
  • Analyzed performance of UK Non-Conforming deals using loan level data. Built an automated framework to download and process data from various servicers and consolidate for modeling.
  • Utilized McDash, Loan Performance, DataQuick and Moody’s Data Buffet data sources to monitor housing market, delinquency pipeline and macro-economic factors in different municipalities.
  • Built a surveillance tool using trustee/servicer reports and scraping web pages.

Confidential, New York, NY

Associate, Fixed Income Commodity and Currency Strategy, Mortgage Strategies

Responsibilities:

  • Inferred loan modifications using 1010data, analyzed re-performance of modified loans. Estimated the impact of loan modifications on non-agency and CDO collateral positions using Slang and SecDb.
  • Built a framework to compare model projections and actual performance using Slang.
  • Helped trading desk identify value by improving non-agency model implemented in C++.
  • Built applications for the trading desk to help them analyze positions and risks using Slang and SecDb.

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