Assoc.Dir.DDIT US&I Analytics Sol. Architecture

Roles and Responsibilities:

• 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.

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 10+ yrs of relevant experience and professional qualifications

Commitment to Diversity & Inclusion:

We are committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.

Accessibility and accommodation

Novartis is committed to working with and providing reasonable accommodation to individuals with disabilities.

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