Top Data & Analytics Jobs in NYC, NY

Prepare and maintain portfolio and fund-level performance reporting and risk metrics for liquid credit strategies. Produce monthly/quarterly/ad hoc client reports, respond to investor data requests, ensure data and process integrity, and coordinate with valuation, fund accounting, investor relations, compliance, and PMs. Support operational due diligence and new product servicing.

Conduct quantitative research and post-trade analytics for securities lending. Develop trading signals, models, algorithms, and automated workflows; own production model monitoring and governance; apply AI and machine learning; and collaborate with traders, researchers, technologists, investors, and risk managers to deliver scalable electronic trading solutions.

Leads technology, data, automation, analytics, and applied AI solutions supporting investment manager research across public and private markets. Translates business requirements into scalable production systems, develops applications and dashboards, improves data quality and governance, and prototypes AI/LLM capabilities. Partners with research, investment, data, technology, operations, and vendor stakeholders while managing complex initiatives, controls, priorities, and adoption.

This internship develops quantitative analytics and financial engineering solutions for BlackRock’s Aladdin platform. Responsibilities include building financial risk models, applying quantitative techniques to investment and client-business problems, developing Python-based solutions, delivering actionable analytics, and collaborating with investment and business teams. Interns gain experience in financial markets, investment products, quantitative research, technology, and the Aladdin platform.

Participate in BlackRock’s quantitative investing internship across quantitative research, portfolio management, product strategy, and investment technology. Apply statistical models, machine learning, Python programming, and large-scale data analysis to investment strategies across asset classes. Learn about capital markets, portfolio trading, economic theory, client objectives, and investment product development while collaborating with research, technology, strategy, and investment professionals.

Support firmwide rollout and execution of Critical Investment Data governance policy. Partner with COOs and data owners to implement standards, run governance forums, deploy tools, collect metrics, deliver training, and drive cross-business collaboration to ensure dataset compliance and measurable business impact.

Develop and deploy analytical and AI-enabled tools integrated with the Aladdin platform to support portfolio construction, investment research, relative value analysis, risk budgeting, due diligence, and portfolio monitoring. Partner with portfolio managers and engineering teams to identify opportunities, prototype solutions, manage delivery, and productionize scalable applications. Communicate insights clearly while applying knowledge of financial markets, asset classes, Python, SQL, and modern technology stacks.

Lead strategy, architecture, and delivery of enterprise data engineering on the Data Cloud Platform. Design batch and real-time pipelines, event-driven streaming, data products, governance, lineage, quality, and observability. Provide technical leadership, mentor teams, drive adoption of standardized data models, CI/CD, and align platform capabilities with business and investment use cases.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.

Lead and establish an Analytics Production Readiness & Assurance (APRA) function to define production standards, data/metadata models, and posture visibility. Partner with product, analytics, and engineering to enforce readiness criteria, surface deviations, and drive accountable decisions. Build technical frameworks, deterministic validation methods, core metrics, and AI-enabled automation to scale oversight. Manage and develop a small technical team and embed decision capture and institutional memory for sustained production operations.