VP, Data Oversight

Job Description

VP, Data Oversight, Investment Management (Pune, India) - Amethyst Partners VP, Data Oversight, Investment Management (Pune, India) A well-established investment management firm is seeking an experienced and dynamic Vice President to lead its Data Oversight team based in Pune. The ideal candidate will be responsible for managing a team of analysts, enhancing data quality frameworks, and driving innovation in data operations across investment functions. This leadership role offers the opportunity to make a significant impact in a data-centric environment supporting global investment decisions. Key Responsibilities Lead and manage a team of data analysts focused on daily data oversight workflows, incident resolution, and continuous improvement of data quality controls. Enhance the data quality rules engine by incorporating advanced exception detection methods, including AI/ML techniques and cross-validation with structured and unstructured data sources. Oversee the onboarding of new datasets, defining data control requirements and minimizing noise in exception reporting. Guide team members through technical development sessions, including code reviews and test design. Produce performance metrics for the team, evaluating effectiveness and efficiency through detailed reporting. Support ad-hoc analytical requests and collaborate closely with engineering teams to test and validate data pipelines. Write and optimize SQL and Python scripts to conduct in-depth data analysis and process automation. Key Requirements At least 12+ years of experience in investment data analysis and data management operations, with SQL / Python scripting experience. Proven team leadership or people management experience. Knowledge of financial data domains such as market data, fundamentals, macroeconomics, transactions, risk, and performance analytics. Proven ability to manage competing priorities with strong organizational and project management skills. Strong command of SQL and Python for data handling and analysis. Academic credentials in Mathematics, Statistics, Engineering, or related quantitative fields. Background in quantitative modeling or systematic portfolio construction is a plus.