AI Product Business Analyst

Position Overview

The AI Product Business Analyst acts as the strategic link between business needs and AI-enabled solutions within the TP.ai FAB ecosystem.

This role supports the design, delivery, and optimization of AI-powered products by gathering requirements, analyzing data, and translating insights into actionable recommendations for engineering, data, and product teams.

Focused on business value, explainability, and ethical compliance, the Analyst ensures that AI features and models deliver measurable impact while maintaining human-centered design principles.

Key Responsibilities

Business Requirement Gathering & Analysis

Collaborate with stakeholders to capture detailed business needs and translate them into functional and non-functional requirements.

Identify opportunities to embed AI capabilities including automation, predictive analytics, conversational AI, and generative features into digital products and business workflows.

Document and maintain end-to-end process and data flows connecting AI agents, APIs, and human users.

Data-Driven Insight & Solution Definition

Analyze business and operational data to uncover trends, patterns, and improvement opportunities.

Support data scientists and engineers by aligning model outputs with real-world business objectives.

Assist in defining and tracking AI success metrics (accuracy, precision, automation rate, confidence thresholds, etc.).

Process Optimization & Modeling

Map existing workflows, identify bottlenecks, and recommend automation or process redesign.

Develop current-state and future-state business process models using standardized frameworks.

Propose solutions that optimize value, cost efficiency, and user experience while maintaining ethical AI practices.

Cross-Functional Collaboration

Act as a liaison between Product, Engineering, Data Science, and Operations to ensure clarity and alignment.

Participate in agile ceremonies grooming, sprint planning, and retrospectives to ensure delivery consistency.

Lead stakeholder workshops and feedback sessions to validate requirements and refine priorities.

Testing, Documentation & Continuous Improvement

Participate in UAT (User Acceptance Testing) and validation of AI-enabled features.

Maintain comprehensive documentation (Business Requirement Documents, User Stories, and Functional Specs).

Track performance post-deployment and recommend iterative improvements based on feedback and analytics.

Ethical & Responsible AI Awareness

Support prompt testing, explainability validation, and bias detection in AI outputs.

Ensure product features adhere to Responsible AI, privacy, and data governance principles.

Balance automation efficiency with human oversight and transparency across all product interactions.

Required Qualifications

Educational Background

Bachelor's degree in Business Administration, Computer Science, Information Systems, Engineering, or a related field.

Certifications in AI Product Management, Data Analytics, or Agile Product Ownership are preferred.

Experience

25 years of experience in Business Analysis, Product Management, or Product Operations roles within digital or technology environments.

Proven ability to support the end-to-end product lifecycle from discovery and requirements gathering to deployment and optimization.

Experience collaborating with Product, Engineering, Data Science, and UX teams in global, matrixed organizations.

Exposure to AI-powered or data-driven products (e.g., conversational AI, predictive models, or automation platforms).

Technical Skills

Working knowledge of AI technologies (NLP, ML, LLMs, computer vision).

Familiarity with AI platforms (Azure AI, OpenAI, Vertex AI, or equivalent).

Understanding of data analytics fundamentals and business intelligence tools (Power BI, SQL, or similar).

Exposure to prompt engineering, AI product testing, and AI evaluation metrics.

Knowledge of ethical AI principles, bias mitigation, and compliance standards.

Soft Skills & Other Requirements

Analytical, curious, and detail-oriented problem solver.

Strong communication skills able to bridge business and technical language fluently.

Collaborative and adaptable in agile, cross-functional settings.

Balances human judgment with data-driven insights.

Ethical, empathetic, and user-focused mindset with commitment to responsible AI.