Associate, Fixed Income AI Analytics

The interview process at Overbond Ltd was smooth, yet difficult. Overbond is an AI quantitative analytics provider for institutional fixed income capital markets based out of three international offices and a client base of numerous global asset managers, hedge funds, investment managers, pensions, insurance companies, and many more.

I was initially contacted by a recruiter from the organization who was interested in arranging a phone interview to discuss in opening in the company. We arranged a date and time, and spoke via phone covering the role, responsibilities, information about the firm, and everything else you would need to know for the role. After a first-round phone interview I was asked to do a group interview via Zoom with numerous members of the firm, including the CEO and Head of Global Sales and Corporate Development, Fixed Income.

Post-interview I was asked to do another presentation for the Fixed Income team, covering the Overbond COBI Bond Pricing AI which algorithmically predicts the most optimal indicative new issue bond price as well as relative value secondary market best-execution bond price for global IG and HY issuers, utilizing machine- learning (ML) algorithms. The ML algorithms analyze millions of data points related to factors such as secondary levels, recent indicative new issue price quotations, company fundamental data elements, investor sentiment and sector comparables. The presentation was a mock-pitch to the Head of Global Fixed Income at a multi-billion dollar Investment Management firm describing the use-case scenarios of Fixed Income artificial intelligence quantitative analytic programs at the trading desk.

Advice? Come prepared, in order to successfully interview at this firm you must come equipped with a very high-level knowledge base surrounding: fixed income, machine learning, artificial intelligence, high yield bonds, curve structure, execution management systems, portfolio management systems, ETF basket pricing, issuance propensity and new issuance modelling, liquidity scoring, margin optimization, screening for mispriced fixed income securities (Overbond’s quantitative method for rich-cheap analysis combines both static and dynamic analysis of multiple factors with AI. It’s a mean-reversion valuation model designed to pre-identify bonds as rich ‘sell’ and cheap ‘buy’ candidates based on proprietary Overbond valuation metrics and AI non- linear optimization), along with pre-trade and post-trade TCA.

A successful candidate would have working knowledge of both sell-side and buy-side fixed income trading, as well as knowledge of electronic trading within fixed income across all G10 currencies.