Head of Quantitative Product Development, QES
Research|New York, NY
Wolfe Research is seeking a Head of Quantitative Product Development to join its Institutional Investor-ranked Quantitative Research, Economics, and Portfolio (QES) team. The employee will report to the Senior Analyst and will be responsible for leading quantitative product development, client consultation and owning the lifecycle of products within QES team.
- Manage development of quantitative models, machine learning algorithms, and software tools used by institutional investors to construct and monitor equity portfolios.
- Transform findings from research to provide productionized products that reflect best practices in stock-selection, portfolio construction, and risk management to the team, clients, and strategic partners.
- Gather, optimize, and maintain extremely large scale (>hundreds of terabytes data) “big data” infrastructure with coverage of 40,000+ securities to provide full coverage of global equity portfolios.
- Develop the overall vision, define technologies, solve operational and technical problems, assess commercial viability, and deliver the products in consultation with business stakeholders.
- Design product components – including risk model builder, attribution library, optimization engine, and back testing functionality – to facilitate typical workflows and common use cases.
- Develop APIs used by QES team and technical clients and UIs used by non-technical clients to access various components of the workflow.
- Leverage relationships with key stakeholders to better position the team to solve complex commercial and technical issues and the products to be innovative relative to competitors.
- Consult with clients and strategic partners on integration of QES products into investment processes and technology platforms.
- Master’s Degree in Financial Engineering, Mathematical Finance, Operation Research, Computer Science, or related fields.
- The ability to communicate highly technical concepts effectively and persuasively to less technical clients, partners, and business stakeholders.
- 2 years in the field with experience in the following areas:
- Global equity risk models, global cross-asset risk models, and multifactor stock-selection models
- Computer programming in R, Python, Java and other major languages
- Machine learning techniques and NLP (Natural Language Processing) algorithms
- Mathematical optimization with dimensionality; portfolio construction techniques (e.g., mean-variance, minimum risk, Black-Litterman, risk parity, maximum diversification)
- Portfolio risk and return attribution
- Global macro forecasting
- Databases and data optimization
- Behavioral finance, asset pricing theory, financial econometrics, and global macroeconomics
- Computational statistics
Interested candidates, please submit your resume via email to Justin Ulman at email@example.com.