Data Research Specialist - Equity Strategy

Acuity Analytics; Bengaluru; Hybrid; INR 2,400,000 - 3,600,000; Full time

Job summary

Acuity Analytics is seeking a senior quantitative equity researcher to lead PIT data infrastructure for fundamentals and estimates, enabling backtest-safe research. You will craft factor scores and validate strategies while supporting model development and client-facing reports.

The role requires 5–7 years in relevant finance analytics, strong SQL, Python (pandas), and experience with large datasets in Snowflake. MBA/ CFA preferred; mentoring a small team will be part of the job.

Qualifications

• 5-7 years of experience in quantitative equity research, data analytics, or related field.
• Strong SQL skills; working proficiency in Python (pandas).
• Deep understanding of PIT data methodology and bias avoidance.
• Familiarity with factor investing concepts and construction methodology.
• Experience with large-scale securities datasets and cloud data warehouses.
• Excellent communication to explain data/methodology to stakeholders.

Responsibilities

• Lead PIT data infrastructure design and maintenance for fundamentals and estimates datasets.
• Conduct quantitative factor research, validation, and performance analysis for equity strategies.
• Support stock selection models, backtesting, and evaluation of inputs.
• Construct and maintain factor scores (value, growth, momentum).
• Own data QC scripts and checks for large-scale securities data.
• Evaluate third-party data vendors (e.g., FactSet) for research use cases.
• Assist production of client-facing research reports; collaborate with strategists.
• Act as liaison for institutional client data/research queries; mentor 1–2 staff.

Skills

SQL

Python (pandas)

PIT data methodology

Survivorship bias awareness

Lookahead bias avoidance

Cloud data warehouses (Snowflake)

Data quality control

Communication skills

Education

MBA (Finance)

CFA charter or progress toward CFA

Tools

Snowflake

FactSet

Polars

Job description

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