Role Purpose:

• Strategic Leadership: Develop and implement analytics solutions that align with business strategies and drive innovation.
• Technical Expertise: Utilize advanced analytics frameworks and tools to deliver high-quality solutions.
• Oversee architectural activities for a US&I Analytics Capabilities domain (GenAI, AIMLOps, NLP, Visualization) and manage the development of solution architectures for projects or programs within the US&I DAI business area.
• Coordinate with other teams to ensure the right business and technical capabilities are incorporated into the solution with an appropriate scaling model for future capacity increases.
• Ensure business processes, requirements & outcomes are defined to drive the analytics platform architecture definition.
• Define standards and direction of architecture in the specific business or technical domain.
• Define and develop the logical design and information management strategies necessary to store, move, and manage data in a new target state.
• Utilize architecture patterns to suggest the most adequate utilization of Data and analytics technical platforms to support the holistic DAI solution architecture design.
• Define, create, and evolve the Architecture Governance Framework (e.g., architecture methods, practices, and standards) for IT.
• Incubate and adopt emerging technologies and launch products/services faster with rapid prototyping & iterative methods to prove and establish value. For identified technologies, launch to enterprise scale, ensuring value is derived.
• Focus and align innovation efforts with the Business strategy, IT strategy, and legal/regulatory requirements.
• Establish and update strategies, implementation plans, and value cases to implement emerging technologies.
• Drive innovation using appropriate people, processes, partners, and tools.

Responsibilities

• Has end-to-end accountability for services and products that are incubated, established, and delivered across cross-functional business areas.
• Serves as point of escalation, review, and approval for key issues and decisions
• Take decisions on the resource and capacity plans in line with Business priorities and strategies and close collaboration with delivery teams
• Decide on continuous improvement within the team
• Decides on the program timeline, governance, and deployment strategy
• Project Management: Oversee the delivery of data lake projects, including data acquisition, quality, transformation, and publishing.
• Collaboration: Work closely with business stakeholders to understand requirements and deliver solutions that meet their needs.
• Innovation: Stay updated with industry trends and emerging technologies to drive continuous improvement.

Skills:

• Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.
• Solid understanding of Analytical and technical frameworks for descriptive and prescriptive analytics.
• Strong familiarity with AWS, Databricks, and Snowflake service offerings.
• Experience integrating disparate analytical and visualization platforms.
• Strong knowledge of MLOps and project life cycle management.
• Strong exposure to data security and governance policy definitions and enforcement capabilities.
• Data product-centric approach to defining solutions. Collaborate with business in gathering requirements, grooming product backlogs, driving delivery, and ongoing data product enhancements.
• Agile delivery experience managing multiple concurrent delivery cycles.
• Sound foundation in Analytical Data life cycle management.
• Awareness of Data product change Management and risk mitigation

Key Performance Indicators:

AI Model Performance:

• Accuracy, precision, recall, and F1 score of deployed AI models.
• Improvement in model performance metrics over time.

AI Solution Deployment:

• Time taken to deploy AI solutions from development to production.
• Number of AI solutions successfully deployed and operational.

Innovation and Technology Adoption:

• Number of new AI technologies and methodologies adopted.
• Time to market for new AI innovations.

Data Management and Utilization:

• Quality and completeness of data used for AI model training.
• Efficiency in data preprocessing and feature engineering.

Scalability and Efficiency:

• Scalability of AI solutions across different business units.
• Resource utilization efficiency (e.g., computational resources, storage).

Governance and Compliance:

• Compliance rate with AI ethics and governance standards.
• Frequency of audits and reviews for AI models and solutions.

Cross-functional Collaboration:

• Number of collaborative projects with other departments (e.g., data science, IT).
• Feedback from stakeholders on the effectiveness of AI solutions.

Business Impact:

• Contribution of AI solutions to business KPIs (e.g., revenue growth, cost reduction).
• ROI from AI projects and initiatives.

User Adoption and Satisfaction:

• User adoption rate of AI solutions.
• User satisfaction scores and feedback on AI tools and applications.

Continuous Improvement:

• Frequency of model retraining and updates.
• Number of improvements made based on user feedback and performance monitoring.

Skills:

• Emerging Technology Monitoring, Consulting, Influencing & persuading, Unbossed Leadership, IT governance, building High Performing Teams, Vendor Management, Innovative & Analytical Technologies.
• Solid understanding of Analytical and technical frameworks for descriptive and prescriptive analytics.
• Strong familiarity with AWS, Databricks, DataIku, Azure AI, and Snowflake analytics service offerings.
• Experience integrating disparate analytical and visualization platforms.
• Strong knowledge of ML Ops and project life cycle management.
• Strong exposure to data security and governance policy definitions and enforcement capabilities.
• Data product-centric approach to defining solutions. Collaborate with business in gathering requirements, grooming product backlogs, driving delivery, and ongoing data product enhancements.
• Agile delivery experience managing multiple concurrent delivery cycles.
• Sound foundation in Analytical Data life cycle management.
• Awareness of Data product change Management and risk mitigation.

Education:

• University Degree and/or relevant experience and professional qualifications