Summary
What you’ll impact
The Senior Quantitative Researcher develops, refines, and maintains complex portfolio construction factor models for investment solutions. The role leverages Python, SQL, and advanced statistical and machine‑learning methods to analyze large, unconventional datasets in a finance context. Responsibilities include backtesting and deploying models in a live trading environment, improving methodologies, and communicating insights to product, engineering, compliance, and legal partners. The researcher works closely with product and engineering teams to launch new investment products while ensuring regulatory compliance.
Responsibilities
What you'll do
- Partner with the Chief Investment Officer and investment leadership to develop, refine, and maintain complex portfolio construction models.
- Work with large datasets, including unconventional data sources, to predict and test statistical market patterns, conceptualize valuation strategies, and improve mathematical models.
- Backtest and implement financial models and signals in a live trading environment.
- Continuously research and analyze new approaches to building portfolio models, applying knowledge of existing and emerging quantitative finance principles, theories, and techniques to inform investment decisions.
- Partner with product and engineering teams to help design and execute on complex projects and product launches.
- Assist with reviews by Legal & Compliance to develop written policies and procedures when new features are introduced or methodology changes are developed.
Requirements
What you’ll bring
- 5+ years of quantitative research, quantitative portfolio construction, or machine learning experience, ideally within asset management, a broker-dealer, an RIA, or broader financial services.
- Experience taking machine learning models, research, or quantitative signals into a live investing or production environment.
- Strong proficiency in Python and SQL for quantitative modeling, statistical analysis, and working with large-scale datasets.
- Strong grounding in statistics, machine learning algorithms, and pattern recognition.
- Outstanding communication skills, with a proven ability to translate complex modeling, statistical, or investment concepts for software engineering, product, and compliance partners.
- Comfort with ambiguity, high personal ownership, and the ability to work independently to deliver on critical business milestones.