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
Key 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
Preferred Qualifications
- 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
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of employee benefits including: retirement investment and tools designed to help you in building a sound financial future; access to education reimbursement; comprehensive resources to support your physical health and emotional well-being; family support programs; and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.
Our hybrid work model
BlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.
Guidance on AI use for candidates
At BlackRock, AI has long been part of how we work – enhancing decision-making, improving operations, and helping us deliver better outcomes for clients. We encourage candidates to use AI thoughtfully to learn, prepare, and work more effectively; but during our interview process, we want to focus on getting to know you through your own experiences, thinking, and judgment. To support you, we’ve provided guidance (opens in new window) on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.
About BlackRock
At BlackRock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.
This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.
To learn more about BlackRock, please visit Careers.BlackRock.com (opens in new window). We also encourage you to get to know us on LinkedIn (opens in new window), Instagram (opens in new window), YouTube (opens in new window), X (opens in new window), and TikTok (opens in new window).
BlackRock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age, disability, race, religion, sex, sexual orientation and other protected characteristics at law.
