Top AI & Machine Learning Jobs

The Principal Data Scientist collaborates on data-driven solutions for AML, enhancing decision-making with machine learning, analytics, and cross-functional partnership.

As a Lead AI Engineer, you will design, develop, and support AI software components while partnering with cross-functional teams to deliver AI-powered products at Capital One.

The Senior Lead AI Engineer will develop and support AI components, lead a cross-functional team, and drive innovation in AI systems at Capital One.

The role involves building machine learning models, collaborating with teams, and leveraging technology to analyze data, ultimately enhancing credit underwriting for business cards.

The Applied Researcher II will develop AI-powered products, partner with cross-functional teams, engage in applied research, and implement AI foundation models.

Lead the Design of Experiments center of excellence, standardizing incrementality measurement, building testing infrastructure, and mentoring Data Scientists while optimizing customer experience and validating product initiatives.

Lead quantitative model risk assessments and develop alternative modeling approaches for loan loss forecasting and ACL. Apply econometric, statistical, and machine learning methods, maintain model accuracy, produce transparent documentation, and communicate technical results to management, regulators, and stakeholders. Leverage cloud and open-source tools, collaborate with model development and risk teams, and drive continuous improvement in modeling practices.

Design, train, validate, and productionize machine learning models for bank operations (NLP, anomaly detection, forecasting, document reading). Partner with engineers and product managers, leverage Python, Snowflake, and cloud platforms, and work with call center operations to build summarization and automation solutions. Research emerging methods (LLMs, transformers), mitigate LLM risks, and produce technical documentation.

Lead and deliver ML solutions for bank operations, partnering with cross-functional teams to design, train, validate, and deploy large-scale models (NLP, forecasting, anomaly detection) using cloud and open-source tools, and mentor technical talent.

Lead design, build, and productionize scalable ML models and pipelines. Collaborate with product and data science teams, implement cloud-based architectures, ensure CI/CD, monitoring, model governance, and responsible AI practices.