Senior Business Analyst

Job Summary

Lead the end-to-end delivery of AI, Machine Learning, and Generative AI initiatives, ensuring successful execution against business and strategic objectives.

Skills and Competencies

• 5-10 years of experience leading the delivery of AI, Machine Learning, or Generative AI initiatives within complex enterprise environments
• Strong expertise in Agile delivery methodologies, including Scrum and Kanban, with the ability to coach teams and drive continuous improvement
• Proven experience managing product backlogs, prioritizing workstreams, and aligning scope, timelines, and quality objectives across multiple teams
• Excellent stakeholder management and communication skills, with the ability to influence decisions, manage risks, and provide clear delivery updates to senior leaders
• Strong understanding of project governance, delivery reporting, and Agile tools such as Jira and Confluence
• Preferred: Familiarity with data science workflows, AWS, enterprise-scale AI deployments, and cross-functional, global team collaboration
• Knowledge of financial analysis and broader finance concepts is advantageous

Education

• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related discipline. Business-related degrees will also be considered when supported by relevant technical experience
• Professional certifications such as PMP, AWS Certified Cloud Practitioner, Scrum Master, SAFe, or equivalent Agile and delivery certifications are preferred

Responsibilities

• Lead the end-to-end delivery of AI, Machine Learning, and Generative AI initiatives, ensuring successful execution against business and strategic objectives
• Develop and manage delivery plans, coordinate cross-functional dependencies, and drive the timely release of high-quality product increments
• Partner with product, engineering, analytics, and data science teams to prioritize roadmaps, support quarterly planning, and align execution with business goals
• Champion Agile delivery practices by facilitating Sprint ceremonies, retrospectives, planning sessions, and continuous improvement initiatives
• Build strong relationships with stakeholders across business and technology functions, providing visibility into progress, risks, dependencies, and outcomes
• Support product lifecycle activities, including pilot programs, product launches, user feedback collection, and adoption initiatives
• Proactively identify delivery risks, implement mitigation strategies, and drive quality standards throughout the development lifecycle
• Collaborate with Data Science, MLOps, Engineering, and Product teams to enhance delivery processes, accelerate time-to-market, and ensure scalable AI solutions and contribute to the evolution of AI-enabled products by translating business needs into actionable delivery plans and measurable outcomes

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