Portfolio Risk Model Data Associate

Responsibilities

• Data Ownership for Portfolio Risk Models
• Ensure input data meets modeling standards for accuracy, completeness, consistency, and timeliness
• Define practical and scalable QC standards aligned with portfolio risk requirements
• Drive improvements in data usability and smooth integration into modeling workflows
• Quality Control & Validation
• Design and prototype data validation rules and QC logic
• Oversee monitoring of both input and derived model data
• Ensure transparency, traceability, and reproducibility of model data
• Apply pragmatic 80/20 thinking to prioritize high-impact data improvements
• Cross-Functional Alignment
• Partner closely with portfolio risk modeling teams to understand evolving data requirements
• Work with data engineering teams to ensure modeling requirements are clearly specified and implemented
• Interface with upstream data teams to resolve inconsistencies and improve reliability
• Drive resolution of cross-team data issues with strong ownership and follow-through
• Research & Data Evolution
• Support onboarding and evaluation of new datasets for modeling and research
• Define governance standards for incorporating structured or unstructured data
• Leverage AI/ML approaches where appropriate to enhance validation or exploratory analysis (a plus, not required)
• Leadership & Communication
• Lead virtual and cross-regional initiatives related to portfolio risk model data
• Provide structured updates and present key data risks or initiatives to senior management when required
• Drive accountability and execution across stakeholders
• Education & Experience
• Bachelor's degree in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative or technical field
• Advanced degree (e.g., Master's) preferred but not required
• 3+ years of relevant experience supporting data in quantitative modeling, risk, or analytics environments
• Strong familiarity with data requirements of financial modeling/analytics
• Experience working with complex financial datasets across global fixed income and/or equity
• Required Skills
• Deep understanding of data lifecycle, QC frameworks, and validation processes in quantitative environments
• Strong grasp of portfolio risk modeling data requirements
• Ability to prototype validation logic (Python/SQL or similar) to clarify and test requirements
• Strong stakeholder management and communication skills
• High accountability and strong execution mindset
• Willingness to engage deeply with detailed, operational data issues

Requirements

• Experience supporting analytics data used in quantitative modeling, risk, or investment decision systems.
• Background in quantitative analytics, data science, or data-focused development
• Knowledge of market data vendors and financial products
• Experience onboarding structured or unstructured datasets
• Exposure to AI/ML techniques for data validation or monitoring
• What Success Looks Like
• Modeling teams receive clean, reliable, well-documented data inputs
• QC frameworks materially improve model robustness
• Data onboarding is efficient and aligned with modeling needs
• Cross-team data issues are resolved quickly and sustainably
• Data workflows feel smooth and predictable to modeling teams

Benefits

Additional Information

About this role We are seeking an associate Data Modeler/Engineer to cover the data domain supporting our global multi-factor Portfolio Risk models across fixed income and equity. This role is responsible for ensuring that data powering our risk models is accurate, well-governed, fit-for-purpose, and operationally smooth for modeling teams. The focus is on data quality, validation, usability, and alignment with modeling requirements - not on owning infrastructure or engineering platforms. The initial emphasis will be on model input data onboarding and quality control, with scope expanding to derived model data, model QC outputs, and research/new data exploration over time. This is a strategic but hands-on role. The individual must be willing to dive into detailed data issues, prototype validation logic when needed, and drive execution across modeling, engineering, and upstream data teams. Domain & Data Scope The portfolio risk models supported by this role span global fixed income and equity portfolios and depend on complex, multi-source data inputs, including: - Market data (prices, yields, spreads, returns) across regions and time zones - Firm fundamentals and issuer-level financial metrics - Bond-level characteristics and reference/security master data - Fixed income analytics such as durations and spreads - Equity returns, factor inputs, and cross-asset pricing series Scope may extend to: - Derived model outputs (factor exposures, covariance matrices, risk decompositions) - Model validation metrics and QC monitoring frameworks - Research and exploratory datasets, including structured and unstructured sources

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