Asset & Wealth Management, MAS, PM Infrastructure and AI Data Strategy, Associate - New York

Multi-Asset Solutions, “MAS” is an investing group within the Asset Management Division at Goldman Sachs. MAS is responsible for managing customized multi-asset class portfolios and funds, including discretionary mandates for US and international public pension plans, sovereign wealth funds and insurance companies and providing outsourced CIO services. The team is comprised of professionals with deep and varied investment backgrounds who specialize in designing and implementing customized multi-asset class portfolios. The team applies rigorous techniques to strategic and tactical asset allocation, asset-liability analysis, portfolio design and implementation, risk management, and portfolio reporting and analytics. The team has been managing customized multi-asset class mandates since 1995 as a committed partner to corporate pensions, sovereign wealth funds, healthcare organizations, endowments, foundations, and government institutions.

Role: 

As a member of the Multi-Asset Solutions Portfolio Management team, this role sits at the intersection of data, AI, and PM Infrastructure—driving the MAS data strategy while partnering directly with the Head of PM Infrastructure & AI Strategy to deliver core platform builds. Working day‑to‑day with Portfolio Managers, Traders, and Risk, you’ll profile and map positions, risk, performance, and reference data to close gaps, improve reliability, and prototype scalable AI‑enabled automation that improves investment workflows and client outcomes.

The ideal candidate is genuinely energized by the transformative potential of AI and emerging technology—driven to self‑experiment, prototype, and push the boundaries of what’s possible with data and intelligent automation. This is a high‑impact role at the intersection of data, technology, and investment management that will shape how MAS sources, governs, and leverages data across every portfolio, workflow, and AI initiative—directly enhancing investment outcomes, strengthening portfolio performance, and elevating client experience

Core Responsibilities: 

• Build deep domain fluency across MAS portfolio management, trading, risk, and PM Infrastructure to translate investment workflows into clear data and technology requirements.
• Own MAS data strategy and governance across structured and unstructured data (standards, definitions, lineage, controls, stewardship) to improve reliability, reuse, and auditability.
• Lead end‑to‑end data discovery and mapping across business functions—positions, risk, performance, and reference data—producing a prioritized roadmap to close data gaps across public and private markets.
• Define build-ready requirements and acceptance criteria for PM Infrastructure & AI initiatives, including data quality thresholds, validation rules, and rollout readiness checks.
• Design and execute rigorous testing and output validation for PM Infrastructure & AI builds (reconciliations, edge cases, regression testing) to ensure results meet business expectations and performance benchmarks.
• Identify and scale high-impact opportunities for data and intelligent automation in PM workflows, partnering daily with PMs, Traders, Risk, and Engineering from prototype through production.
• Create executive-ready reporting for the Head of PM Infrastructure & AI Strategy—recurring status updates and dashboards that translate technical progress into decisions, risks, and next steps for senior leadership.

Basic Requirements:

• Bachelor's degree required in a quantitative, technical, or finance-related field (e.g., Computer Science, Data Science, Statistics, Economics, Engineering, or Mathematics).
• 2–4 years of relevant experience in investment management, data science, business intelligence, project management, or a data-focused role within financial services or asset management.
• Excellent project management skills
• Solid understanding of data concepts including data modeling, data quality frameworks
• Experience with Python similar scripting languages for data manipulation and analysis is a plus.
• Advanced knowledge of AI/ML concepts, including how data quality and structure impact model performance.
• Strong written and verbal communication skills, with the ability to document technical processes clearly and present findings to both technical and non-technical audiences.
• Highly organized and detail