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

Bachelors degree in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative or technical field

Advanced degree (e.g., Masters) 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

BlackRocks 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, weve 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 childrens 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. Its why were 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.