Director, Data Science and AI/ML

Director, Data Science and AI/ML

Data Strategy

US Exempt Regular

Full time

Stateside Exempt 4.4

Location: Stateside Remote

We are seeking an experienced and dynamic Director of Data Science and AI/ML to lead our data science, AI and machine learning initiatives within a fast-paced, data-driven organization. This role demands a blend of deep technical expertise in machine learning, AI, and data engineering, combined with strong business acumen and strategic leadership. You will build and oversee a team of data scientists, AI, and machine learning engineers, guiding end-to-end solution delivery—from ideation, specification, and model development to scalable deployment, operationalization, monitoring, and continuous improvement—primarily leveraging Databricks.

As a hands-on leader, you will balance strategic vision with direct technical involvement, driving the implementation of robust MLOps and AIOps processes that ensure reliability, efficiency, and continuous improvement of AI/ML solutions in production. A key aspect of your role will be building trust and credibility with both business stakeholders and technical teams to foster collaboration, alignment, and shared success.

Duties and Responsibilities:

Leadership & Strategy

• Define and execute the vision and roadmap for data science, AI, and ML capabilities aligned with business objectives.

• Lead, mentor, and grow a high-performing team of data scientists and ML engineers, fostering collaboration, trust, and continuous learning.

• Collaborate with cross-functional business leaders to identify, prioritize, and formalize AI/ML use cases and requirements, producing both formal and evolving specification and operational documents.

• Establish best practices, processes, and governance for model development, deployment, and lifecycle management, including AIOps and MLOps workflows.

• Lead User Acceptance Testing (UAT) processes, ensuring delivered solutions meet business needs and quality standards.

• Champion iterative continuous improvement cycles, balancing innovation with risk management and consistent value delivery.

• Build strong relationships and gain trust with business and technical teams by demonstrating transparency, reliability, and responsiveness.

Technical Oversight & Hands-on Contribution

• Oversee the end-to-end AIOPs & MLOps lifecycle on Databricks, including data ingestion, feature engineering, model development, validation, deployment, and continuous monitoring.

• Lead the design, implementation, and refinement of MLOps pipelines and AIOps frameworks on Databricks to automate experimentation, deployment, versioning, monitoring, and alerting of models in production.

• Implement infrastructure and tooling for automated model testing, rollback, and retraining triggered by performance degradation or data drift, ensuring operational robustness and scalability.

• Ensure seamless collaboration between data scientists (focused on algorithm/model innovation and validation) and ML engineers (focused on scalable deployment, optimization, and MLOps infrastructure).

• Develop and enforce structured model handover contracts covering performance metrics, latency, memory footprint, and operational constraints to ensure smooth transitions from development to production teams.

• Participate hands-on in performance optimization, code reviews, and troubleshooting of AI/ML solutions in production environments.

Collaboration & Communication

• Foster a culture of transparency and teamwork, promoting close collaboration between data scientists, AI/ML engineers, software engineers, and business stakeholders.

• Communicate complex technical concepts and AI/ML performance and value insights effectively to technical and non-technical audiences.

• Engage proactively with stakeholders to manage evolving requirements and expectations.

• Build and maintain trust with diverse teams by being approachable, dependable, and delivering on commitments.

• Manage stakeholder expectations and project timelines within a SAFe Agile environment while balancing innovation with operational excellence.

Innovation & Continuous Improvement

• Stay abreast of emerging trends and technologies in AI, machine learning, MLOps, and AIOps, applying them strategically to maintain competitive advantage.

• Demonstrate quick learning aptitude to continuously incorporate new tools, methods, and industry best practices while leveraging opportunities and managing risks.