Core Risk Governance, RQA, Associate

Business Overview 

Understanding and managing risk is the cornerstone of BlackRock’s approach to responsible investing. The Risk & Quantitative Analysis (RQA) group provides independent oversight of BlackRock’s fiduciary and enterprise risks. Our mission is to advance the firm’s risk management practices and to deliver independent risk expertise and constructive challenges to drive better business and investment outcomes. RQA promotes BlackRock as a leader in risk management by providing independent top-down and bottom-up oversight to help identify investment, counterparty, operational, regulatory, technology, and third-party risks. 

RQA is committed to investing in our people to increase both individual enablement and a strong collaborative environment. As a global group located all around the world, our goal is to create a culture of inclusion which encourages teamwork, innovation, diversity, and the development of our future leaders.  We actively engage in discussions on career growth and work with team members to understand how personal passions and strengths connect with our purpose. 

Role Description: 

Core Risk Governance responsible for delivering firm‑wide risk oversight, governance, and control effectiveness across investment strategies and asset classes. The team plays a central role in ensuring the integrity of risk frameworks, portfolio setup controls, and governance processes by leveraging data, advanced analytics, automation, and AI‑enabled solutions at scale. 

The function focuses on centralising risk governance activities, strengthening preventive controls, and embedding technology‑driven oversight across the investment lifecycle. By building standardized data frameworks, deploying AI‑ and analytics‑based controls, and delivering transparent, timely reporting, Core Risk Governance enables consistent decision‑making and robust escalation across the platform. 

The team partners closely with Investment Risk, Technology, Data, Compliance, and Control functions to modernise risk governance—moving from manual, fragmented processes to systematic, scalable, and explainable AI‑enabled solutions that enhance risk visibility, control effectiveness, and operational resilience across Core RM. 

Key Job Responsibilities: 

• Architect, build, and scale AI driven solutions—from ideation and proof of concept to production deployment—to transform risk and data processes through advanced automation, intelligent exception management, and predictive insights. 

• Write production grade code in Python to build data pipelines, automation frameworks, analytics libraries, and AI/ML components; ensure solutions are maintainable, tested, and deployable. 

• Design, build, and maintain resilient data pipelines (batch and streaming) with strong engineering controls, including SLAs, dependency management, backfills, replay/re ingest capabilities, and automated alerting.. 

• Define and enforce data persistence, schema standardization, lineage, and metadata management, ensuring traceability, auditability, and long term reliability of analytical outputs. 

• Implement data quality and control frameworks, including automated validation checks, reconciliation to source, threshold based monitoring, and structured exception queues with clear escalation path. 

• Develop scalable analytics and reporting solutions, translating complex datasets into clear, explainable, and decision ready outputs for senior stakeholders. 

• Partner closely with cross functional teams (technology, data, risk, compliance) to embed governance, controls, and resilience directly into technical and AI enabled solutions 

• Continuously drive automation, process efficiency, and platform resilience, reducing manual intervention while improving accuracy, timeliness, and scalability. 

Experience, Skills and Qualification: 

• Bachelor’s/Masters degree in Engineering, Computer Science, Data Science, Mathematics, or a related quantitative discipline. 

• 3–6 years of hands‑on experience in Python‑based analytics and AI/ML, including building data pipelines, developing models/LLM/agent workflows, and integrating outputs into business processes. 

• Strong data engineering and data governance foundations, including data quality controls, lineage, metadata management, and control frameworks.. 

• Proficiency in advanced analytics, with experience with tech platforms 

• Ability to design and implement reporting solutions for portfolio monitoring, stress testing, and regulatory compliance. 

• Strong stakeholder management and communication skills to collaborate across investment, technology, and compliance teams. 

• Experience in automation and process optimization for reporting and analytics workflows.