Top AI & Machine Learning Jobs

This role involves leading data science projects, developing machine learning models, collaborating with stakeholders, and mentoring other data scientists.

The AI Solutions Architect develops ML solutions, integrates them into systems, collaborates with stakeholders, and reviews code while maintaining technical architecture.

Own end-to-end decision intelligence product domains: build, deploy, and monitor ML models and behavioral signals; lead experimentation and causal measurement; develop generative AI solutions and personalization; partner with product and engineering to define roadmaps and reusable ML platform assets that drive measurable business outcomes.

Lead strategy and execution for enterprise intelligence by framing complex AI, data, and technology questions; partner with senior leaders to design and deploy intelligence products; create executive- and Board-level communications; run cross-functional initiatives and governance; and support enterprise planning, portfolio management, and adoption of AI-enabled capabilities.

The role involves designing, building, and maintaining an MLOps platform, developing workflows for ML applications, and integrating various technologies while supporting agile teams.

The Director of AI & Data Science will lead AI/ML initiatives, manage the Model Development Life Cycle, and mentor technical teams, while overseeing core AI technical solutions and standards.

The Senior Associate - Data Scientist will develop AI and machine learning solutions, focusing on model lifecycle management and delivering AI initiatives in collaboration with business teams.

The AI Solution Architect designs enterprise architectures for AI solutions, ensuring they align with business objectives and technology standards while guiding implementation and governance.

As a Data Science Lead, you'll drive AI/ML initiatives, mentor data scientists, collaborate with stakeholders, and ensure effective model deployment and governance.

The role involves model validation, monitoring AI model performance, challenging model developers, and translating technical requirements into practical evaluations and controls for AI and ML models.