Who You’ll Work With: The Equity Investment Management Technology (EIMT) Research team develops the software that powers research, portfolio management, and quantitative analysis across AB's equity platform. You will join a small, high-impact group that owns QuantM — AB's proprietary alpha factor research library. The team sits at the intersection of quantitative finance, data infrastructure, and AI integration, partnering directly with equity PMs, quant researchers, and data engineers. You will report to the Head of Equity Quant Model Technology and collaborate with senior quant researchers across the firm What You’ll Do: · Design, develop, and maintain quantitative research tools, factor libraries, and analytical workflows that support equity investment strategies across the firm · Build and optimize large-scale data pipelines using Python, SQL, and Snowflake, with deployment on Azure Cloud infrastructure · Collaborate closely with quantitative researchers, portfolio managers, and data engineers to translate research requirements into scalable, production-grade solutions · Develop enriched research datasets — including multi-horizon returns, security-level metadata, and sector/country classifications — to support flexible cross-sectional analysis · Contribute to AI and machine learning initiatives that enhance the firm's quantitative research capabilities · Deliver business intelligence dashboards and visualization tools that surface actionable insights for investment teams What We’re Looking For: · Experience in quantitative finance technology, with direct exposure to equity research infrastructure, factor libraries, or systematic investment platforms · Strong proficiency in Python (including Pandas and NumPy) and SQL; experience with Snowflake is preferred · Experience building and maintaining large-scale data pipelines and analytical workflows in a cloud environment (Azure, AKS, or similar) · Solid understanding of alpha factors, backtesting methodologies, portfolio optimization, and equity research data including pricing, fundamentals, and classifications · Familiarity with security master frameworks, vendor data integration (e.g., Refinitiv, S&P), and cross-dataset reconciliation · Strong communication and collaboration skills, with the ability to work effectively alongside senior quantitative researchers, portfolio managers, and non-technical stakeholders · Bachelor's degree in Computer Science, Engineering, Mathematics, Finance, or a related quantitative discipline; Master's degree preferred Location: Pune, India Pune, India