Voya Investment Management is looking to develop, implement, and enhance quantitative models and analytics to support their Equities platform, aiming to identify alpha opportunities and improve investment strategies.
Requirements
- Hands-on experience across broad range of modern analytic and data tools, particularly Python (numpy/pandas, machine learning packages such as XGBoost and SKLearn) with a solid understanding of relational and non-relational databases.
- Experience using one of the Big 3 cloud-based computing services (Azure, AWS, Google Cloud) and data science development platforms (e.g., Databricks, AzureML) is a plus.
- Strong analytical and mathematical skills.
- Excellent working knowledge of econometrics and statistics.
- Familiarity with financial statement analysis is a plus.
- Experience with financial databases such as Compustat, Worldscope, Factset, Clarifi/CapitalIQ, Axioma, Barra, Bloomberg is a plus.
- Experience in factor-based equity investing including ESG metrics is a plus.
Responsibilities
- Work on innovative research projects aimed at advancing our proprietary multi-factor and machine intelligence models through the addition of new alpha signals or better model estimation techniques, both linear and nonlinear.
- Explore enhancements to portfolio optimization and risk management to improve the risk-adjusted net returns of our systematic investment strategies or target specific investment outcomes for our clients.
- Evaluate new data sets from a range of sources for their potential to generate alpha.
- Help maintain and advance the team’s shared codebase, data repositories, and cloud-based technology stack that is the cornerstone of our research and production processes.
- Contribute to new product development in collaboration with our client-facing and product teams.
- Contribute to thought leadership articles to enhance awareness of our team’s investment philosophy and capabilities.
Other
- 5-7 years of relevant work experience in applied quantitative research, preferably investment management.
- Attention to detail, curious, and self-motivated.
- Ability to work independently and collaboratively within the broader research team, as well as prioritize tasks.
- Excellent problem solving, interpersonal and communication skills.
- Ability to translate quantitative insights into actionable process improvements
