Hiring, Investment Management and Risk Data Product Owner - ISS Data (Associate Director) Role The Investment and Risk & Attribution Data Product Owner role is instrumental in the creation and execution of a future state design for investment and risk data across our key business areas. The successful candidate will have an in-depth knowledge of all data domains that services Investment management, risk and attribution capabilities within the asset management industry. The role will sit within the ISS Delivery Data Analysis chapter and fully aligned to deliver cross functional ISS Data Programme in Technology, and the candidate will leverage their extensive industry knowledge to build a future state platform in collaboration with Business Architecture, Data Architecture, and business stakeholders. The role is to maintain strong relationships with the various business contacts to ensure a superior service to our clients. Key Responsibilities Leadership and Management: Lead the Investment and Risk data outcomes and capabilities for the ISS Data Programme. Realign existing resources and provide coaching and line management for junior data analysts within the chapter, influence and motivate them for high performance. Define the data product vision and strategy with end-to-end thought leadership. Lead data product documentation, enable peer-reviews, get analysis effort estimation, maintain backlog, and support end to end planning. Be a catalyst of change for improving efficiencies and innovation. Data Quality and Integrity: Define data quality use cases for all the required data sets and contribute to the technical frameworks of data quality. Align the functional solution with the best practice data architecture & engineering. Coordination and Communication: Senior management level communication to influence senior tech and business stakeholders globally, get alignment on the roadmaps. An advocate for the ISS Data Programme. Coordinate with internal and external teams to communicate with those impacted by data flows. Collaborate closely with Data Governance, Business Architecture, and Data owners etc. Conduct workshops within the scrum teams and across business teams, effectively document the minutes and drive the actions. Essential Skills Required Strong leadership and senior management level communication, internal and external client management and influencing skills. At least 15 years of proven experience as a senior business/technical/data analyst within technology and/or business change delivering data led business outcomes within the financial services/asset management industry. 5-10 years s a data product owner adhering to agile methodology, delivering data solutions using industry leading data platforms such as Snowflake, State Street Alpha Data, Refinitiv Eikon, SimCorp Dimension, BlackRock Aladdin, FactSet etc. In depth knowledge of how data vendor solutions such as Rimes, Bloomberg, MSCI, FactSet support Investment, Risk, Performance and Attribution business needs. Outstanding knowledge of data life cycle that drives Investment Management such as research, order management, trading, risk and attribution. In depth expertise in data and calculations across the investment industry covering the below. Financial data: This includes information on asset prices, market trends, economic indicators, interest rates, and other financial metrics that help in evaluating asset performance and making investment decisions. Asset-specific data: This includes data related to financial instruments reference data like asset specifications, maintenance records, usage history, and depreciation schedules. Market data: This includes data like security prices, exchange rates, index constituent and licensing restrictions on them. Risk data: This includes data related to risk factors such as market risk, credit risk, operational risk, and compliance risk. Performance & Attribution data: This includes data on fund performance returns and attribution using various methodologies like Time Weighted Returns, Transaction based performance attribution. Should possess Problem Solving, Attention to detail, Critical thinking. Technical Skills: Hands on SQL, Advanced Excel, Python, ML (optional) and knowledge of end-to-end tech solutions involving data platforms. Knowledge of data management, data governance and data engineering practices. Hands on experience on data modelling techniques like dimensional, data vault etc. Willingness to own and drive things, collaboration across business and tech stakeholders.
