AI Business Analyst

S
SupportNinjaAI Enablement
Philippines, Night shift (Able to work on shifting schedules)Full-TimeMiddle
Salary not disclosed
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Job Details

Languages
English
Experience
2+ years
Required Skills
Project ManagementArtificial IntelligenceBusiness AnalysisData AnalysisMachine LearningPrompt EngineeringGenerative AIProcess improvement

Requirements

  • 4-year college degree or equivalent business experience
  • At least 2+ years of experience in Business Analysis, Process Improvement, Project Management, and Data Analysis
  • Good written and oral communication skills in English
  • Proficiency in requirements gathering and documentation
  • Expertise in process mapping and workflow analysis
  • Experience with gap analysis and root cause analysis
  • Experience in business process improvement
  • Experience in user story and use case creation
  • Proficiency in User Acceptance Testing (UAT)
  • Strong stakeholder management skills
  • Understanding of Artificial Intelligence (AI) and Machine Learning (ML) concepts
  • Knowledge of Generative AI tools (e.g., ChatGPT, Copilot, Gemini)
  • Ability to identify business processes suitable for AI and automation
  • Understanding of prompt engineering and AI workflow design
  • Understanding of AI risks, governance, and ethical considerations
  • Basic understanding of databases and cloud platforms

Responsibilities

  • Work closely with stakeholders across business, engineering, security, compliance, legal, and operations teams
  • Understand current processes, tooling, and data flows across the organization
  • Gather and document business, operational, functional, and security requirements related to AI initiatives and tooling
  • Document current-state workflows and end-to-end processes across departments
  • Map systems, data sources, ownership, access permissions, and data flows
  • Identify process pain points, dependencies, operational constraints, and inefficiencies
  • Conduct gap analysis across existing processes, tooling, and workflows
  • Identify data readiness gaps including data access, governance, quality and ownership
  • Support prioritization of findings, dependencies, and recommendations for AI enablement
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