Director, Automation & AI​/MLOps

Automation and AI/ML Ops Delivery:

Oversee the design, development, and testing and deployment of automation and AI/ML operations that support data products for use in advanced analytics, data science, and AI initiatives. Ensure platforms are capable of handling complex data workflows and high-volume data processing. Guide the technical vision and strategy for an accurate, scalable, usable, and reliable data platform and infrastructure.

Service Leadership:

Manage a team of service leads, fostering a culture of collaboration, innovation, and excellence aligned to McKesson’s ICARE and ILEAD values. Provide mentorship and guidance to team members to ensure professional development and growth. Hire and retain top data engineering talent, set performance expectations, conduct regular assessments, and foster a collaborative and innovative work environment.

Strategic Leadership in Automation and AI/ML:

Contribute to development, implementation of enterprise data strategy that aligns with McKesson’s business objectives, emphasizing the strategic use of data as a key asset in driving business outcomes. Lead engineering practices and standards workstream.

Innovation in Data Technologies and Practices:

Lead the adoption of cutting-edge data technologies and methodologies to enhance data accessibility, quality, and insights. This also involves selecting appropriate technologies, tools, and platforms to ensure efficient data flow, data integration, and data governance. Lead PoC studies to bring and operationalize new features and capabilities.

Cross-Functional Collaboration and Integration:

Foster strong collaboration with business units, IT, and analytics teams to ensure data engineering practices meet evolving business needs. Develop and delivery data solutions backed by strong cross functional collaboration.

Governance, Compliance, and Data Security:

Implement engineering solutions to support robust data governance policies and practices to ensure data quality, compliance with global data protection regulations (e.g., GDPR, CCPA), and the security of sensitive and proprietary information. Ensure quality of enterprise data assets is maintained and enhanced by implementation of modern, automated processes.

Vendor and Stakeholder Management:

Manage relationships with technology vendors and partners to ensure the company has access to the best tools and services.

Minimum Requirements

Typically requires 15+ years of professional experience and 6+ years of diversified leadership, planning, communication, organization, and people motivation skills (or equivalent experience).

Critical Skills

• 12+ years of experience in a technology role; proven experience in a leadership role, preferably in a large, complex organization.

• Leading large data / technical teams – Data/ML Ops Engineering, Solution Architects, and Business Intelligence Engineers, encouraging a culture of innovation, collaboration, and continuous improvement.

• Hands-on experience building and delivering Enterprise Data Solutions

• Extensive market knowledge and experience with cutting edge Data, Analytics, Data Science, ML and AI technologies

• Strong knowledge of machine learning algorithms. Statistical models, and data science tools

• Extensive professional experience with Big Data systems, data pipelines and data processing

• Proven experience in leading AI/ML teams and implementing successful projects

• Deep expertise in Data Architecture, Data Modeling, and task estimations.

• Familiarity with data privacy standards, methodologies, and best practices

• Define and track key performance indicators (KPIs) to measure the efficacy/impact of automation and AI/ML initiatives while presenting on the progress and outcomes of key projects to executive leadership

• Ensure that automation and AI/ML practices comply with relevant regulations and ethical standards through staying informed about legal considerations in AI/ML, advocating for responsible and transparent practices

• Practical hands-on experience with data technologies and Cloud Platform like Hadoop, Hive, Redshift, Big Query, Snowflake, Databricks, GCP and Azure.

• Hands on experience working on analytical platforms like SAS, R, Python, Azure…