Data Research Specialist - Equity Strategy

Role & responsibilities

• Lead point-in-time (PIT) data infrastructure design and maintenance for fundamentals and estimates datasets, ensuring backtest-safe, lookahead-free data for factor research
• Conduct quantitative factor research factor construction, validation, and performance analysis — supporting the firm's published equity strategy research
• Support the quant/strategy team on equity stock selection models, including backtesting and evaluation of fundamental factor inputs
• Construct and maintain factor scores (value, growth, momentum) to identify most-favored and least-favored stocks within the coverage universe
• Own data quality control processes: build and maintain QC scripts/checks for large-scale securities data (fundamentals, estimates, pricing)
• Evaluate and select third-party data vendors (e.g., FactSet) for research use cases, assessing coverage, methodology, and fit
• Support production of client-facing research reports and notes, working closely with strategists
• Serve as a direct point of contact for institutional client data/research queries, both from internal teams publishing client-facing content and from external clients directly
• Lead and mentor a small team (1-2 members), overseeing delivery quality and workload prioritization

Preferred candidate profile

• 5 to 7 years of experience in quantitative equity research, data analytics, or a related field within financial services
• Strong SQL skills; working proficiency in Python (pandas)
• Deep understanding of point-in-time data methodology, survivorship bias, and lookahead bias avoidance
• Familiarity with factor investing concepts (value, momentum, quality, etc.) and factor construction methodology
• Experience with large-scale securities/fundamentals datasets and cloud data warehouses, particularly Snowflake
• Strong SQL skills, with working proficiency in Python (pandas, polars); experience in transitioning workflows for data analysis and validation preferred
• MBA (Finance), CFA charter or progress toward CFA strongly preferred
• Excellent client communication skills ability to explain data/methodology to both technical and non-technical stakeholders