Top Data & Analytics Jobs

As a Senior Associate Data Scientist, you will leverage technologies like Python, AWS, and Spark to build machine learning models for US card acquisitions. Collaborating with cross-functional teams, you will develop, deploy, and monitor models, translating complex data insights into actionable business strategies while driving continuous innovation.

As a Data Scientist, you will collaborate with cross-functional teams to build machine learning models for credit card acquisitions, leveraging a wide range of technologies to analyze large datasets. Your role includes model development, deployment, and translation of complex insights into business value, while maintaining a customer-first approach and driving innovation.

As a Senior Associate Data Scientist at Capital One, you will build machine learning models for credit card customer acquisitions, collaborating with cross-functional teams and leveraging your skills in Python, AWS, and other technologies to analyze large datasets and deliver customer-focused solutions.

The role involves leveraging analytic skills to build data solutions and tackle business challenges in the Consumer Bank. Responsibilities include using open-source technologies for data mining, developing BI tools, ensuring data quality, and collaborating with tech teams for data governance.

The Principal Data Analyst will leverage analytic and technical skills to innovate and maintain data solutions. Responsibilities include using Open Source technologies for data mining, developing data visualization tools, and ensuring data quality management while collaborating with various stakeholders.

As a Lead Data Engineer at Capital One, you will design, develop, and support technical solutions while collaborating with Agile teams. You will work with cutting-edge technologies, manage cloud-based data services, and mentor engineering team members to drive innovation and enhance financial empowerment through data solutions.

The Distinguished Machine Learning Engineer will lead AI/ML initiatives focused on developing production-scale machine learning applications. Responsibilities include collaborating with cross-functional teams, mentoring engineers, and implementing innovative solutions driven by a deep understanding of AI and machine learning technologies, ensuring optimal performance and high availability of applications.