About this role

Systematic Active Equity (SAE) is the quantitative equity group within BlackRock's Systematic investment group. We invest our client assets using a systematic investment approach. SAE is a pioneer and thought leader in the industry and has consistently achieved client investment goals across global equity markets for over 30 years. We believe research and innovation are critical to continuing our success and believe in a multi-disciplinary approach that intersects traditional finance and economics with data and computer science.

Responsibilities:
Execute portfolio rebalances and generate trade lists aligned with model views and evolving market conditions.
Conduct performance attribution to assess signal effectiveness and risk-factor contributions.
Enhance model design, portfolio construction, and implementation through systematic research.
Identify and monitor key factor exposures and event risks; develop scalable processes to manage emerging risks.
Advance proprietary analytics tools by creating visualizations and automating repetitive workflows.
Oversee investments across the entire signal lifecycle-from alpha research and portfolio construction to execution and attribution.
Engage with internal and external research (e.g., academic papers, conferences, sell-side reports) to identify novel data sources and strategy ideas.
Lead the development, deployment, and monitoring of systematic equity signals.
Apply machine learning frameworks to scale feature discovery, construction, selection, and combination.
Implement state-of-the-art NLP techniques and LLM workflows across unstructured text data and related tasks.

Skills & Qualifications:
0-2 years of experience in the financial industry.
Strong foundation in statistical and machine learning methodologies.
Hands-on experience with large-scale data processing using SQL and Python.
Proficient in Python (Pandas, NumPy), Scikit-Learn, XGBoost/LightGBM, TensorFlow, and PyTorch.
Familiarity with Unix-based systems, AWS (EC2, EMR, S3), Hadoop, and common data transfer protocols (FTP/SFTP).
Deep understanding of financial economics, portfolio construction, analytics theory, and behavioral finance.
Proficient in managing and querying structured and unstructured datasets, including data ingestion from internal/external databases.
Applied experience in natural language processing and large language model (LLM) pipelines, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG).
For San Francisco, CA Only the salary range for this position is USD$111,625.00 - USD$132,500.00 . Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.

Our benefits

To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents 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 on when and how to use AI during our hiring process so you can approach each step with confidence and showcase your best self.