AI Business Analyst

Overview

Summary:

The AI Business Analyst plays a key role in bridging business needs with AI/ML solutions. This role involves understanding complex business processes, identifying AI-driven opportunities, defining detailed requirements, and collaborating with technical teams to deliver scalable AI solutions—particularly within Retail Banking and Consumer Finance.

The ideal candidate will bring strong analytical skills, domain expertise, and experience working with AWS-based AI/ML platforms to translate business problems into actionable solution designs.

Required Experience & Skills

• 8–10+ years of experience as a Business Analyst, preferably in AI/ML, data analytics, or digital transformation projects.
• Strong domain knowledge in Retail Banking and Consumer Finance, including customer lifecycle, lending, payments, risk, and compliance.
• Working experience with AWS cloud services and familiarity with AI/ML workflows.
• Ability to interpret data outputs, model insights, and translate them into business actions.
• Strong documentation skills: BRDs, FRDs, user stories, process flows, and requirement traceability.
• Excellent communication, problem-solving ability, and stakeholder management.

Key Responsibilities

• Partner with business stakeholders to identify AI/ML opportunities across retail banking and consumer finance—e.g., fraud detection, customer segmentation, credit risk analysis, collections strategy, and customer experience improvement.
• Gather, document, and validate detailed business requirements, user stories, and acceptance criteria for AI/ML initiatives.
• Work closely with data scientists, AI architects, and engineering teams to ensure business needs are accurately translated into AWS‑enabled AI solutions (SageMaker, Redshift, Lambda, S3, etc.).
• Support development of business cases, ROI models, and impact assessments for AI-led initiatives.
• Evaluate data quality, understand financial datasets, and ensure compliance with regulatory and risk guidelines.
• Facilitate workshops, demos, and UAT cycles with cross-functional teams.
• Monitor solution adoption and performance, ensuring outcomes align with business objectives.