AI/Analytics Product Manager

Required Skills

Product Management

Experience managing AI, Analytics, or Data products throughout the product lifecycle. Strong product strategy, roadmap planning, prioritization, stakeholder management, and execution skills. Experience translating business requirements into product features and technical capabilities. Strong understanding of customer-centric product development and outcome-based delivery. Experience defining KPIs, OKRs, success metrics, and product analytics.

Agile Delivery

Hands-on experience with Agile/Scrum methodologies. Experience managing product backlogs, user stories, sprint planning, release planning, and incremental product delivery. Strong collaboration across cross-functional engineering, architecture, data, and business teams.

Agentic AI & Generative AI

Experience designing, building, and managing autonomous AI agents, including single-agent and multi-agent systems. Hands-on experience with agentic frameworks such as LangGraph or similar orchestration frameworks. Knowledge of LLM orchestration, tool calling, function calling, planning, reasoning, workflow decomposition, and AI agent lifecycle management. Experience building AI solutions using Retrieval-Augmented Generation (RAG) architectures. Knowledge of vector databases, embeddings, semantic search, hybrid retrieval, prompt engineering, grounding strategies, and context optimization. Experience implementing AI-powered automation, self-healing workflows, anomaly detection, automated root-cause analysis, and intelligent remediation capabilities. Understanding of Responsible AI, AI governance, model evaluation, observability, and monitoring.

Data Science & Analytics

Strong understanding of Data Science lifecycle, statistical analysis, machine learning concepts, predictive analytics, and experimentation. Experience working with structured and unstructured data to derive actionable business insights. Knowledge of feature engineering, model evaluation, model performance monitoring, and AI/ML solution lifecycle. Experience partnering with Data Scientists to operationalize AI and machine learning models into production. Strong analytical skills with expertise in SQL, data visualization, KPI development, and business intelligence. Experience leveraging analytics to drive product decisions and measure business impact.

Data & Cloud Technologies

Experience with BigQuery, SQL, cloud-native analytics platforms, and modern data architectures. Understanding of data pipelines, data engineering concepts, APIs, event-driven architectures, and data governance. Experience working on Google Cloud Platform (GCP) is preferred. Familiarity with MLOps, CI/CD pipelines, containerization (Docker, Kubernetes), and AI deployment best practices.

Leadership Expectations

Strong strategic thinking with the ability to balance long-term vision and execution. Excellent communication and stakeholder management skills across business and technology organizations. Demonstrated ability to lead cross-functional teams without direct authority. Passion for innovation, continuous learning, and delivering measurable business outcomes through AI and analytics. Hands-on mindset with the ability to work closely with engineering and data science teams to shape solution architecture, validate technical approaches, and drive successful product delivery.

Key Responsibilities

Own the end-to-end product strategy, roadmap, and execution for AI & Analytics capabilities supporting the Digital Account & Consent Platform. Translate business problems into scalable AI, analytics, and data products that improve customer experience, operational efficiency, and business value. Lead product discovery, prioritization, roadmap planning, and backlog management using Agile methodologies. Collaborate with Engineering, Data Science, Data Engineering, Security, Privacy, UX, Architecture, and Business stakeholders to deliver enterprise-grade AI solutions. Define product KPIs, success metrics, experimentation strategies, and continuously optimize product performance using data-driven insights. Drive adoption of AI-powered automation, predictive analytics, and intelligent decision-making capabilities across the platform. Ensure AI products comply with enterprise security, privacy, governance, responsible AI, and data management standards. Stay current with emerging AI technologies and identify opportunities to accelerate innovation across Ford’s Digital ecosystem.

Preferred Qualifications

Experience with Customer Identity, Digital Account, Consent Management, Customer Data Platforms (CDP), or Customer 360 solutions. Experience delivering enterprise AI or GenAI products at scale. Knowledge of LLMOps, MLOps, AI observability, and production AI systems. Familiarity with ML frameworks, fine-tuning approaches (PEFT/LoRA), and model lifecycle management. Experience building AI-powered recommendation, personalization, search, or intelligent automation solutions. Knowledge of enterprise architecture, cybersecurity, privacy regulations, and data governance.