Portfolio Risk Modeler Data Lead, Vice President II

BlackRock•New York, NY

9h•$170,000 - $225,000•Hybrid

About The Position

We are seeking a VP-level Data Lead to drive the data domain supporting global multi-factor Portfolio Risk models across fixed income and equity. This role is responsible for end-to-end execution and ownership of data quality, validation, and usability across the modeling data lifecycle. The VP will partner closely with modeling, engineering, and upstream data teams to ensure that data powering portfolio risk models is robust, well-governed, and aligned with modeling requirements. The role combines strategic judgment with hands-on execution, with an initial focus on model input data onboarding and quality control, expanding over time to derived data, QC frameworks, and integration of new datasets.

Requirements

8–12+ years supporting data in quantitative modeling, risk, or analytics environments
Strong familiarity with global fixed income and/or equity datasets
Experience driving data initiatives across multiple teams and workflows
Deep understanding of data lifecycle, QC frameworks, and validation processes
Strong grasp of portfolio risk modeling data requirements
Ability to prototype and validate data logic (Python/SQL or similar)
Strong stakeholder management and execution focus
High ownership, attention to detail, and delivery mindset

Nice To Haves

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
Derived model data (factor exposures, covariance matrices, risk decompositions)
Model validation metrics and QC monitoring frameworks
Research and exploratory datasets, including structured and unstructured sources

Responsibilities

Own the data domain for portfolio risk models, ensuring high standards of data quality and usability
Ensure data meets requirements for accuracy, completeness, consistency, and timeliness
Define and evolve scalable QC frameworks aligned with modeling needs
Drive improvements in data integration into modeling workflows
Design and implement data validation rules and QC logic
Establish monitoring across input and derived model data
Ensure traceability, documentation, and reproducibility of model data
Prioritize improvements based on impact to model performance and stability
Partner with portfolio risk modeling teams to translate requirements into data solutions
Collaborate with data engineering teams to define and implement data pipelines
Engage with upstream data providers to improve data quality and reliability
Drive resolution of data issues across teams with strong ownership
Lead onboarding and evaluation of new datasets for modeling and research
Define governance approaches for structured and unstructured data integration
Support adoption of advanced techniques (including AI/ML where relevant)
Drive execution across global, cross-functional stakeholders
Provide clear, structured updates on data quality, risks, and initiatives
Promote accountability and strong execution standards across partners