Top Data & Analytics Jobs

Lead quantitative analyses to design, implement, and evaluate Medicare and Medicaid payment models. Query large claims/enrollment datasets, build payment calculations, financial and quality metrics, and apply ML/econometric/predictive models. Mentor staff, write reproducible code and documentation, develop client relationships, perform rapid ad-hoc analyses, and help integrate AI into technical workflows.

Lead design and implementation of scalable data solutions and pipelines using Python, SQL, and PySpark. Partner with stakeholders to translate requirements, perform deep-dive analysis on healthcare and claims data, design data models and transformations, build dashboards, and deploy AI/LLM-assisted workflows. Ensure data quality, compliance, governance, and mentor junior analysts.

Use advanced analytics, data manipulation, visualization, and predictive modeling to extract insights for financial risk and markets clients. Build and maintain data pipelines, validate data quality, create dashboards (Power BI/Tableau), apply machine learning and statistical models, and collaborate with teams to support client engagements and decision-making.

Lead design and implementation of enterprise fraud prevention strategy and decisioning engines for loan products. Use data analytics and machine learning to assess fraud and identity risk, recommend verification standards, set KPIs, report to senior leadership, and lead/mentor a team to automate and improve fraud prevention while maintaining a frictionless customer experience.

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Lead control and risk management for Data & Analytics products and platforms. Ensure adherence to the CORE program, embed AI/ML and data governance controls, manage Risk Assessment Structures, support control testing and issue remediation, develop control documentation, partner with Legal/Compliance/OpRisk/Audit, and support regulatory responses while leading and advising stakeholders.

Lead Data Architect responsible for defining and evolving data architecture and standards across 100+ applications, driving adoption of strategic data lake platforms, designing target-state architectures and data exchange patterns, mentoring junior architects, and ensuring governance, data quality, and cloud-based database best practices.

As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.

Lead enterprise data engineering strategy and roadmap to build scalable cloud-native data platforms. Oversee design and modernization of data architectures, pipelines, and integration. Ensure data security, privacy, and governance, incorporate AI/ML, and drive data democratization. Lead global teams, collaborate with business and technology leaders, and deliver enterprise-wide data capabilities to enable analytics, innovation, and business growth.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.

As Head of Investment Data, you will define and drive data strategy, governance, and operations to enhance investment decision-making and client outcomes.

Lead architecture for data, reporting, and analytics product family: set target-state designs, guide technology evaluation and AI-enabled integrations, produce and review production code, enforce governance, resiliency, security, and auditability, and provide technical leadership across teams and vendors.

Designs and owns enterprise data quality platforms and tooling, translating governance requirements into scalable technical architectures. Assesses platforms and integration patterns (batch, event-driven, API), documents architecture decisions, ensures non-functional requirements, and partners with product and platform teams to deliver governed, auditable data quality solutions.

Build and maintain analytics data systems and pipelines that capture, transform, and integrate product usage data at scale. Deliver accurate insights and visualizations to inform product decisions, collaborate cross-functionally, and apply AI tools to improve analytics and workflows.

Develop and validate commercial credit risk models (rating, loss forecasting, stress testing, pricing). Collaborate with business, data infrastructure, and governance teams to deploy, monitor, and document cloud-based model solutions and derive business insights from large datasets.

Lead data management for marketing execution: design targeting and measurement strategies, build repeatable datasets, automate reporting, implement monitoring and quality checks, enable responsible AI-supported workflows, and quantify value from automation and audience improvements.

The EO Imagery Analyst will provide geospatial and imagery analysis to improve data operations for ML algorithm testing, working with EO imagery products and metadata.

The Geospatial SAR Imagery Analyst Scientist analyzes SAR data to improve machine learning algorithms, curating imagery for data sets and recommending strategies for data gaps.

The EO Imagery Geospatial Analyst/Scientist provides geospatial expertise, conducts assessments, analyzes data formats, and supports ML algorithm testing.

Responsible for validating market risk models and ensuring their accuracy. Engage with various stakeholders, communicate findings, and maintain model integrity while leveraging innovative analytical technologies.

Senior Solution Architect responsible for strategy, solution and data architecture across application, platform, and data domains. Designs scalable architectures, data models, integration patterns, and data platforms (warehouses, lakes, streaming, cloud). Partners with business and engineering teams to ensure architectures meet functional, nonfunctional, governance, security, and regulatory requirements while enabling analytics, AI/ML, and transformation.

Lead analytics and machine-learning efforts to prevent point-of-sale fraud: analyze large datasets, design and implement fraud decisioning capabilities, partner with risk modeling and cross-functional teams, build KPI monitoring and automation, and communicate findings and project outcomes to senior leadership.

Lead a global analytics team to transform financial and operational data into actionable insights. Partner with stakeholders to align analytics with strategy, build scalable Power BI dashboards integrated with Snowflake, apply predictive modeling and ML, enforce data governance, drive automation with Python and Power Automate, and improve data architecture and reporting to inform decision-making and business performance.

Conduct rigorous research using statistics and machine learning to extract patterns from datasets. Implement algorithms, back-test models, and document findings.