ICT AI Business Analyst/Data Analyst

Role Summary The AI Business Analyst (AI BA) bridges business stakeholders and technical teams to identify, define, and deliver AI/ML and generative AI solutions that create measurable business value. This role translates business problems into clear requirements, supports data and model readiness, helps manage delivery from discovery through adoption, and ensures solutions meet governance, risk, and compliance expectations.

Key Responsibilities:

• Partner with stakeholders to understand objectives, pain points, and decision processes; translate into AI use cases and user stories.
• Facilitate discovery workshops; document current-state processes, target outcomes, and measurable KPIs/OKRs.
• Define functional and non-functional requirements for AI products (accuracy, latency, explainability, human-in-the-loop, monitoring, and auditability).
• Collaborate with data engineering and analytics to identify data sources, data quality needs, labeling requirements, and feature definitions.
• Support model lifecycle planning with product/ML teams (training approach, evaluation metrics, drift monitoring, retraining triggers).
• Draft and maintain documentation: business requirements, process flows, acceptance criteria, test cases, and release notes.
• Coordinate UAT and operational readiness; validate outputs with SMEs and ensure adoption plans are in place (training, comms, support).
• Contribute to AI governance activities: risk assessments, privacy and security reviews, model documentation, and compliance alignment.
• Track delivery progress, dependencies, and risks; communicate status and facilitate decision-making.
• Measure post-launch performance and benefits realization; identify enhancements and new opportunities.

Basic Qualifications:

• Bachelor's degree in Business, Information Systems, Data/Analytics, Computer Science, or related field
• Minimum 3 years of experience in business analysis, product analysis, or a related role delivering data/analytics or software solutions.
• Demonstrated ability to write clear requirements (epics/user stories/acceptance criteria) and map processes.
• Working knowledge of AI/ML concepts (supervised vs. unsupervised learning, evaluation metrics, model drift) and/or generative AI concepts (prompting, retrieval-augmented generation, hallucinations/grounding).
• Experience partnering with technical teams (data engineering, data science, software engineering) using Agile delivery methods.
• Strong stakeholder management, facilitation, and communication skills; able to explain technical topics to non-technical audiences.

Preferred Qualifications:

• Experience delivering AI products in production, including monitoring and continuous improvement.
• Hands-on analytics skills (SQL; familiarity with Python/R; dashboarding tools such as Power BI/Tableau).
• Familiarity with cloud data/AI platforms (e.g., Azure, AWS, GCP) and MLOps concepts.
• Knowledge of responsible AI practices (bias/fairness considerations, explainability, privacy-by-design).
• Domain expertise in the business area supported (e.g., finance, supply chain, customer service, manufacturing, healthcare).
• BA/PM certifications (CBAP, PMI-PBA, PSPO/CSPO, SAFe) are a plus.

Role Summary The AI Business Analyst (AI BA) bridges business stakeholders and technical teams to identify, define, and deliver AI/ML and generative AI solutions that create measurable business value. This role translates business problems into clear requirements, supports data and model readiness, helps manage delivery from discovery through adoption, and ensures solutions meet governance, risk, and compliance expectations.

Key Responsibilities:

• Partner with stakeholders to understand objectives, pain points, and decision processes; translate into AI use cases and user stories.
• Facilitate discovery workshops; document current-state processes, target outcomes, and measurable KPIs/OKRs.
• Define functional and non-functional requirements for AI products (accuracy, latency, explainability, human-in-the-loop, monitoring, and auditability).
• Collaborate with data engineering and analytics to identify data sources, data quality needs, labeling requirements, and feature definitions.
• Support model lifecycle planning with product/ML teams (training approach, evaluation metrics, drift monitoring, retraining triggers).
• Draft and maintain documentation: business requirements, process flows, acceptance criteria, test cases, and release notes.
• Coordinate UAT and operational readiness; validate outputs with SMEs and ensure adoption plans are in place (training, comms, support).
• Contribute to AI governance activities: risk assessments, privacy and security reviews, model documentation, and compliance alignment.
• Track delivery progress, dependencies, and risks; communicate status and facilitate decision-making.
• Measure post-launch performance and benefits realization; identify enhancements and new opportunities.

At Stellantis, we assess candidates based on qualifications, merit, and business needs. We welcome applications from all people without regard to sex, age, ethnicity, nationality, religion, sexual orientation, disability, or any characteristic protected by law. We believe that diverse teams reflect our identity as a global company, enabling us to better address the evolving needs of our customers and care for our future.