Experience

Kiavi

• United States
• Financial Services
• 200 - 300 Employee

• Senior Data Analyst

  • Jun 2017 - Apr 2020



General Assembly

• United States
• Higher Education
• 700 & Above Employee

• Data Science Student

  • Jul 2016 - Oct 2016

  A three-month immersive program where I learned data science fundamentals and put them into practice in weekly solo and collaborative projects and a final capstone project. Skills and knowledge acquired include: Python, data munging, exploratory data analysis, regressions, classification, supervised and unsupervised machine learning, algorithms, data visualization, Bayesian statistics, web scraping, and libraries such as pandas, numpy, sklearn, matplotlib, seaborn, and… Show more A three-month immersive program where I learned data science fundamentals and put them into practice in weekly solo and collaborative projects and a final capstone project. Skills and knowledge acquired include: Python, data munging, exploratory data analysis, regressions, classification, supervised and unsupervised machine learning, algorithms, data visualization, Bayesian statistics, web scraping, and libraries such as pandas, numpy, sklearn, matplotlib, seaborn, and others. Capstone project: Could machine learning learn music composition fundamentals without being explicitly given those rules of composition? To answer this question, I built a model to generate music in the style of Johann Sebastian Bach chorales (a type of composition for four voices). Using a fairly no-frills version of this model, I was able to generate new music in the same style. The result was very simple but music composition fundamentals COULD be recognized by the listener! In other words, the algorithm DID learn those fundamental music composition rules, even though I never explicitly identified what they were. Show less A three-month immersive program where I learned data science fundamentals and put them into practice in weekly solo and collaborative projects and a final capstone project. Skills and knowledge acquired include: Python, data munging, exploratory data analysis, regressions, classification, supervised and unsupervised machine learning, algorithms, data visualization, Bayesian statistics, web scraping, and libraries such as pandas, numpy, sklearn, matplotlib, seaborn, and… Show more A three-month immersive program where I learned data science fundamentals and put them into practice in weekly solo and collaborative projects and a final capstone project. Skills and knowledge acquired include: Python, data munging, exploratory data analysis, regressions, classification, supervised and unsupervised machine learning, algorithms, data visualization, Bayesian statistics, web scraping, and libraries such as pandas, numpy, sklearn, matplotlib, seaborn, and others. Capstone project: Could machine learning learn music composition fundamentals without being explicitly given those rules of composition? To answer this question, I built a model to generate music in the style of Johann Sebastian Bach chorales (a type of composition for four voices). Using a fairly no-frills version of this model, I was able to generate new music in the same style. The result was very simple but music composition fundamentals COULD be recognized by the listener! In other words, the algorithm DID learn those fundamental music composition rules, even though I never explicitly identified what they were. Show less



Moody's Investors Service

• United States
• Financial Services
• 700 & Above Employee

• Lead Data Analyst and Business Analyst (concurrent), Servicer Quality Ratings

  • 2012 - Jul 2016

  I lead the data team. We provide accurate and insightful analytics that are the backbone of the rating committee process. Data quality is paramount. I also identify data technology projects with business and collaborate with developers to make it happen. This includes creating product specifications, thinking about the target audience, considering the development complexity, and managing timelines. • Provide expertise to business to enhance the use of data in rating committees and… Show more I lead the data team. We provide accurate and insightful analytics that are the backbone of the rating committee process. Data quality is paramount. I also identify data technology projects with business and collaborate with developers to make it happen. This includes creating product specifications, thinking about the target audience, considering the development complexity, and managing timelines. • Provide expertise to business to enhance the use of data in rating committees and publications • Conceived of and implemented improved data intake process for 25 mortgage servicers. • Successfully led data conversion project to a new data source, increasing our subject population by over 100%. Defined project parameters, created specifications, performed QA. • Initiated data automation project to save over 1000 hours a year.

• Assistant Vice President (concurrent), Servicer Quality Ratings

  • 2007 - Jul 2016

  I am the lead analyst for 10-15 mortgage servicers, spanning over 20% of the US mortgage market. I evaluate how well mortgage servicers operate, information that mortgage investors value highly. Our analysis comprises a qualitative piece, which includes management interviews and process reviews, and a quantitative piece, which includes comparative performance metrics. • Lead internal presentations of my rating analysis to colleagues and management • Lead and co-author timely… Show more I am the lead analyst for 10-15 mortgage servicers, spanning over 20% of the US mortgage market. I evaluate how well mortgage servicers operate, information that mortgage investors value highly. Our analysis comprises a qualitative piece, which includes management interviews and process reviews, and a quantitative piece, which includes comparative performance metrics. • Lead internal presentations of my rating analysis to colleagues and management • Lead and co-author timely press releases, extensive operational servicer reports, and leading industry research. • Led successful project to publish inaugural dashboard publication dashboard. • Received Moody’s Structured Finance Research Award in April 2013.

• Analyst, Residential Mortgage Backed Securities

  • 2005 - 2007

  I served as lead rating analyst on residential mortgage-backed transactions. I was responsible for credit, cashflow, structural, legal, and operational analysis, which I brought to internal rating committees. • Contributed to leading industry research. • Earned the honor of being one of the team's cashflow model checkers



Volti, CappellaSF

• San Francisco Bay Area

• Professional Singer

  • 2003 - 2016

  I currently sing with in the Bay Area with Volti (www.voltisf.org), CappellaSF (www.cappellasf.org), and Chalice Consort (chaliceconsort.com). I also sing with the NY-based Baroque trio, Charites (www.charitesmusic.com), with whom I performed at the 2014 Berkeley Early Music Fringe Festival . I currently sing with in the Bay Area with Volti (www.voltisf.org), CappellaSF (www.cappellasf.org), and Chalice Consort (chaliceconsort.com). I also sing with the NY-based Baroque trio, Charites (www.charitesmusic.com), with whom I performed at the 2014 Berkeley Early Music Fringe Festival .



PwC

• Professional Services
• 700 & Above Employee

• Associate in Structured Finance

  • Sep 2004 - Nov 2005

  I provided agreed-upon procedure work on structured finance transactions, working closely with investment bankers and trustees. Such work included cashflow modeling, payment verification, and collateral due diligence. Asset types include CDOs, CLOs, and student loan transactions. I provided agreed-upon procedure work on structured finance transactions, working closely with investment bankers and trustees. Such work included cashflow modeling, payment verification, and collateral due diligence. Asset types include CDOs, CLOs, and student loan transactions.