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.