Top Data & Analytics Jobs

Provides analytics-driven advisory support for mergers, acquisitions, and other business transactions. Analyzes contract data to identify risks, obligations, performance insights, and compliance indicators; develops algorithms, data models, pipelines, visualizations, and machine learning solutions. Integrates multiple data sources, validates data integrity and security, communicates findings to clients, and mentors junior team members.

Develops regulatory compliance analytics and reporting solutions using SQL, dashboards, governed metrics, semantic layers, and AI-enabled business intelligence. Builds and maintains Tableau or similar dashboards, automates reporting with Tableau and Alteryx, evaluates Machine Learning and GenAI opportunities, supports prompt development, and translates stakeholder needs into analytical products. Collaborates with Compliance, Risk, Technology, Finance, and senior governance leaders to improve regulatory engagement insights, adoption, and firm-wide decision-making.

Monitor fraud trends in retail deposit acquisition, analyze large datasets, identify defensive gaps, and develop strategies balancing fraud losses, operational costs, and customer impact. Partner with cross-functional and technical product teams to deliver fraud prevention capabilities, present actionable insights to senior leadership, and support business growth while protecting customers.

Leads the design, development, and maintenance of enterprise data systems supporting manufacturing data, analytics, reporting, and AI. Oversees scalable cloud-based solutions, data pipelines, infrastructure, modeling, formats, and frameworks. Partners with stakeholders to align data capabilities with business needs and manages a high-performing data engineering team through coaching, performance management, and organizational development.

Analyze large datasets to identify digital fraud trends, develop loss-reduction strategies and business rules, automate fraud alert processes, monitor emerging risks, and present actionable insights to senior leadership. Collaborate with technical and business teams on fraud-fighting products, apply experimentation and machine learning, manage financial impacts, and improve customer experience across digital payment channels.

Leads data-driven strategic initiatives, performs root-cause analysis and data mapping, and identifies automation opportunities. Develops ETL workflows, REST APIs, dashboards, KPI reporting, and actionable insights from structured and unstructured data. Collaborates cross-functionally with operations, technology, finance, compliance, and controls teams. Manages Agile data analytics projects, improves processes, and presents business cases, metrics, and technical findings to senior management.

Manage point-of-sale fraud strategies and conduct complex risk analyses to reduce losses while balancing customer impact. Responsibilities include developing fraud rules and policies, identifying risk indicators, enhancing reporting, testing mitigation processes, leading strategy initiatives end-to-end, analyzing large datasets, and presenting recommendations to management. The role collaborates with Operations, IT, Risk Management, and other cross-functional partners to improve fraud detection and customer experience.

Semiconductor yield analysis internship focused on reviewing experimental data, identifying device performance shifts and process anomalies, improving measurement accuracy, and using inline data and machine learning to predict cell performance. The intern will collaborate with process integration, process engineering, and data science teams while developing root-cause analysis, process improvement, and technical communication skills.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Leads healthcare analytics service delivery across Medicare, Medicaid, and Commercial programs. Designs analytic models, dashboards, reports, and predictive solutions; translates complex data into client-ready insights; guides data governance and management practices; and promotes AI, machine learning, generative AI, and automation to improve reporting efficiency. Partners with clients, analysts, developers, subject matter experts, and AI teams to analyze claims, enrollment, provider, and member data and support value-based payment initiatives.

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.

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.

Leads data governance and ownership initiatives, defining data requirements, quality standards, metadata, lineage, controls, and curated datasets. Performs SQL-based testing, validation, reconciliation, and root-cause analysis. Partners with technology and business teams to mitigate data risk, support analytics and machine learning use cases, and improve data products. Applies AI and LLM tools to automate governance activities and contributes to roadmap, agile delivery, and stakeholder management.

Lead quantitative analytics initiatives by integrating and preparing diverse customer, financial, digital, account, and operational datasets. Analyze customer journeys, identify friction points, optimize collections features, develop KPIs, and apply statistical, machine learning, and predictive modeling techniques. Translate strategic objectives into analytics solutions, collaborate with product and technology teams, and present actionable insights to business and executive stakeholders.

Partner with Total Rewards stakeholders to design and execute analytics that evaluate employee wellbeing and benefits program ROI. Combine and validate data from claims, utilization, and other sources, build forecasting and advanced-analytics models, and deliver dashboards and executive summaries. Lead project workstreams, mentor junior analysts, and ensure data quality, compliance, and actionable insights to inform benefits strategy.

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.

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.

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.

Develop and enhance capital markets models, including deposit, derivatives, and fixed-income models. Build quantitative and machine learning solutions using large datasets and cloud technologies to improve business performance. Own model development from conceptualization through validation, deployment, training, documentation, and monitoring. Provide technical guidance, communicate statistical findings to diverse audiences, and collaborate with finance, business, and cross-disciplinary teams.

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.

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.

A 10-week quantitative analytics internship for PhD candidates. Interns apply statistics, artificial intelligence, machine learning, and programming to credit risk, fraud detection, operational loss, portfolio forecasting, model validation, and consumer decisioning projects. The role includes technical training, mentorship, collaboration with quantitative professionals, and exposure to senior leaders. High-performing interns may be considered for full-time roles after graduation.