Top Data & Analytics Jobs in NYC, NY

Leads strategic and quantitative analysis for Commercial Risk Management, supporting credit risk model development, underwriting, loss forecasting, and performance improvements. Evaluates model prototypes using data visualization and storytelling, translates technical findings into business recommendations, tests hypotheses, develops monitoring plans, and partners with model developers, credit officers, underwriters, finance, and relationship managers to implement solutions.

Provides enterprise data risk advisory services, oversees data governance assessments, identifies and mitigates risks, challenges business processes and controls, and advises senior stakeholders. The role partners across data governance, metadata, data quality, and stewardship functions; uses analytics and risk metrics to drive organizational change; and oversees projects and matrixed team members while remaining an individual contributor.

Analyze commercial banking challenges and opportunities, develop strategic recommendations, model lending and pricing strategies, assess credit risk and portfolio performance, support new product launches, and collaborate with IT, Sales, Operations, and Finance to improve profitability and growth.

Analyze complex data and develop managed data solutions, business intelligence tools, dashboards, metrics, and self-service capabilities. Partner with business and technology teams to deliver strategic insights, manage data quality, metadata, lineage, security, and access governance. The role requires scripting, querying, visualization, Snowflake development, and familiarity with cloud services and data governance.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data and full-stack solutions. The role uses Java, Scala, Python, databases, distributed computing, streaming technologies, and cloud data warehouses. Responsibilities include collaborating across Agile teams, developing scalable solutions, conducting unit tests and code reviews, optimizing performance, keeping current with emerging technologies, and mentoring engineers.

Leads data engineering initiatives by designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborates with Agile teams, product managers, and machine learning engineers; works with distributed systems, databases, streaming applications, and data warehouses. Performs code reviews and unit testing, optimizes performance, explores emerging technologies, and mentors engineering team members.

Leads business analytics strategy, data infrastructure, product analytics, measurement frameworks, experimentation, and data-driven strategy for AI-powered customer assistance channels. Connects customer experience and operational efficiency outcomes while partnering with Product, Technology, Operations, and Finance. Manages and develops analysts, oversees concurrent projects, builds business requirements, and drives executive-level decisions supporting growth, profitability, and service improvements.

Leads data analysis strategy for an analysis platform product, including data mining, modeling, testing, reporting, process improvement, and product analytics. Manages junior analysts, mentors team members, collaborates across business units, and supports large-scale databases and data storage systems. The role includes business intelligence, strategic experimentation, operational efficiency improvements, and consulting on production and data system design.

Leads technical strategy for enterprise data pipelines and data-sharing platforms. Builds scalable, resilient, high-performance systems using AWS, lakehouse architecture, Kafka, Flink, Spark, Snowflake, and Databricks. Develops code, drives engineering excellence and technology adoption, influences enterprise stakeholders, advises on platform capabilities, mentors engineers, and recruits technical talent. The role requires deep data engineering and architecture expertise, hands-on leadership, and innovation across internal and external data environments.

Develop, implement, govern, and integrate enterprise ontologies and data models. Harmonize linked data, establish semantic and data-standardization guidelines, support knowledge organization and machine-learning integrations, and advocate semantic technology across Finance and Enterprise teams. Use W3C ontology standards and specialized tools to test, troubleshoot, and maintain models while collaborating with Technology, Product, and Machine Learning stakeholders.