Data Research Specialist - Equity Strategy

Moodys Analytics; Bengaluru; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Acuity Analytics is seeking a Data Research Specialist to support equity strategy research, combining quantitative work, data infrastructure, and client enablement. You will design PIT data pipelines, run factor research, and backtest inputs for institutional clients.

The role involves mentoring a small team, coordinating with strategy and engineering, and delivering timely client-facing research. Bangalore-based, long-term embedded engagement.

Qualifications

• 5–7 years of experience in quantitative equity research or data analytics.
• Strong SQL skills and proficiency in Python (pandas).
• Familiarity with point-in-time data methodology and bias avoidance.

Responsibilities

• Lead PIT data infrastructure design for fundamentals and estimates datasets.
• Conduct quantitative factor research and performance analysis.
• Support equity stock selection models with backtesting and data inputs.
• Construct and maintain factor scores to identify stock coverage patterns.
• Own data quality control processes for large-scale securities data.
• Evaluate third-party data vendors (e.g., FactSet) for research use cases.
• Produce client-facing research reports and notes with strategists.
• Act as liaison between research and engineering teams.
• Oversee a small team and manage workload priorities.

Skills

Quantitative equity research

Data analytics

SQL

Python (pandas)

Polars

Lookahead bias avoidance

Factor investing concepts

Snowflake

FactSet

Education

MBA (Finance)

CFA charter or progress toward CFA

Tools

Snowflake

FactSet

Job description

Job Details

• Position Title: Data Research Specialist - Equity Strategy
• Experience Level: 5 - 8 Years
• Department: Investment Operations & Risk Services (IORS)
• Location: Bangalore
• Position reports to: Associate Director

Job Summary

Acuity Analytics is seeking a Data & Research Specialist to join our team on a long-term embedded engagement with a leading global investment bank's equity strategy research group. The role sits at the intersection of quantitative research, data infrastructure, and client relationship management, supporting factor-based research production for institutional clients.

Key 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
• Act as a bridge between the firm's strategy research team and engineering/technology teams, translating research requirements into data/infrastructure specifications
• Support the monthly production cycle, ensuring accuracy and timeliness of recurring deliverables. Serve as a point of contact for client interactions related to production and research queries

Key Competencies

• 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