Top Data & Analytics Jobs

The Manager of Regulatory Submissions will lead regulatory strategies and submissions for global authorities, ensuring regulatory alignment with business goals while collaborating with cross-functional teams on medical devices.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

Develop, deploy, and monitor models and pattern-recognition algorithms to detect fraud across lending products. Write Python for analysis and modeling, query complex datasets with SQL, partner with Fraud Operations to refine features and reduce false positives/negatives, conduct ad hoc analyses, and communicate findings to support verification and underwriting improvements.

Lead data ownership and governance for wholesale credit risk. Define data strategies, ensure data quality, metadata and lineage, enable data products, mitigate data risks, and coordinate cross-functional delivery for analytics and reporting.

Drive card fraud strategy through data analysis, trend monitoring, and analytics-driven recommendations. Partner with product, technology, and operations to design, test, and implement detection and treatment solutions while balancing fraud mitigation, financial performance, customer experience, and operational efficiency.

Lead design, validation, and monitoring of fraud strategies using data analysis and AI/ML. Partner with product, technology, and operations to develop detection capabilities, test implementations, and balance fraud mitigation with revenue, cost, and customer impact.

Lead data ownership and governance for Corporate Finance and Treasury: define business data meaning, drive data strategy execution, improve data quality and controls, manage delivery timelines, mitigate data risks across the lifecycle, and support analytics by governing data used in analytics platforms.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Lead data ownership for customer application and account opening data: define and document data requirements, model and productize datasets, enable NLP/LLM querying, support data engineering delivery, mitigate data risks, and manage direct or matrixed staff to drive analytics and AI/ML use cases.

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 analytics-focused engagements from intake through delivery, partnering with senior leaders to translate business needs into scalable analytics, automation, and AI-enabled solutions, align stakeholders, drive measurable business value, and standardize enterprise solutions across large, matrixed organizations.

Lead analytics-driven, cross-functional initiatives from intake through delivery. Partner with senior leaders to translate business needs into analytics, automation, and AI-enabled solutions, align stakeholders, manage delivery, and scale reusable enterprise solutions that drive measurable business value.

Lead a team to develop and execute data analytics strategy for aerospace finance operations. Build data pipelines, apply statistical and advanced analytics, create dashboards, collaborate cross-functionally, and drive process improvements and cost savings while ensuring data quality and mentoring analysts.

Lead development and execution of a unified software quality data strategy for Electronic Systems. Build automated ETL pipelines, real-time dashboards, alerts, and analytics; coordinate across engineering teams and sites; deliver data-driven insights to leadership; and enable continuous improvement of software quality in regulated, embedded aerospace environments.

Lead and develop a team delivering data conversion for customer implementations. Drive adoption of AI and automation, manage complex/at-risk migrations, track KPIs, and improve processes through experimentation and transformation.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Design, build, and operate scalable data platforms and ETL/ELT pipelines using Python, SQL, Airflow, and AWS. Optimize data warehouses, ensure data quality and governance, implement CI/CD and infrastructure-as-code, and collaborate with cross-functional teams while mentoring engineers.

Lead data science projects to optimize drug development using AI. Develop models for patient selection and clinical trial evaluations while collaborating with cross-functional teams to drive insights.

Lead and architect high-value healthcare analytics engagements, build reusable analytic frameworks (DBT), partner with product and engineering, mentor analysts, ensure analytic quality, and represent Arcadia externally to drive population health and value-based care outcomes.

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.

Develop and implement software solutions for automated trading systems, working with quantitative researchers on complex financial challenges.

As a PhD intern, you'll design, develop, and deploy software solutions for automated trading, collaborating with the Quantitative Research team.

Design, develop, and deploy software solutions for automated trading systems while collaborating with researchers on technology priorities.

As a Senior Quantitative Analyst, you will develop and enhance modeling frameworks, apply quantitative methods, and collaborate to build cloud-based solutions for Capital Markets at Capital One.

Lead reporting and analytics for a business domain during a multi-year transformation. Own data migration, define business requirements, prioritize reporting roadmap, partner with tech/data teams and vendors, deploy reporting/analytics solutions (including ML-driven automation), measure KPIs, and drive adoption and value realization across cross-functional stakeholders.