Job Description

Specific Responsibilities: Participate in development of the next generation platform for options strategies. Assistance in factor research and risk-management approaches unique to option investment strategies Development of front-end tools to aid portfolio optimization, monitoring, trade building and trade routing. Onboarding data from various internal and external sources Simplification, automation, and support of existing manual processes What makes this role unique or interesting (if applicable) AB investment units are making a significant investment in building out a platform to improve our ability implement systematic investment strategies. This project is still in its early stages, and the candidate will be given significant opportunities to make contributions at the ground-level. The candidate will have an opportunity to work alongside an established team of developers, quantitative analysts and portfolio managers to create a derivatives strategies platform encompassing quantitative modeling, portfolio construction, performance attribution, data consolidation and quality control. Qualifications, Experience, Education: Undergraduate degree in Engineering (required); Masters in Engineering, Economics, or Finance (preferred). CFA/FRM (pursuing or completed) is a plus. Proficiency in Python and one other programming language (MATLAB preferred, or R/C++) is required. Strong understanding of statistical analysis and techniques (hypothesis testing, distributions, regression modeling) is required. Machine learning, Natural Language Processing methods is a strong advantage Academic-level exposure to option pricing methods is required. High attention to detail, accuracy, and ability to work independently and as part of a team. Experience in quantitative analysis with exposure to multiple asset classes (equities, fixed income, commodities, currencies) is a plus. Experience with large data sets and SQL for data processing. Knowledge of macroeconomic and broad capital markets (equities, credit, rates, volatility) is a plus. Experience in asset management and factor research is an advantage. Familiarity with financial databases and tools (FactSet, Bloomberg) is required. Ability to quickly learn new tools, multi-task, and thrive in a fast-paced environment. Special Knowledge (if applicable): Nice to have: Experience with Git/GitHub Experience working with risk models/attribution tools such as BarraOne, Experience with market data vendors - Bloomberg, QADirect, Barclays POINT, etc Experience with machine learning or big data Experience working in the finance industry, demonstrable curiosity in quantitative research and investment