Vice President II, AI Domain & Insights Orchestration

Circana

4d•$250,000 - $280,000•Hybrid

About The Position

We are seeking a senior leader to own the end-to-end vision and build of our AI-powered "digital teammate" for Client Insights—Starfinder— to help them more effectively serve our Consumer Packaged Goods (CPG) and General Merchandise clients. This role spans large language model (LLM) architecture and orchestration, domain knowledge capture at the individual and organizational level, human-in-the-loop training, and productization across our solutions portfolio. The leader will manage a team of consultants who will design mechanisms to efficiently elicit and encode expert know-how from our Client Insights teams and clients, govern how that knowledge is weighted and validated, and drive trusted diagnostics and actionable recommendations that leverage our market measurement data and agents (e.g., data access, assortment optimization).

Requirements

10+ years in AI/ML-enabled products or decision-support systems, with 2+ years architecting LLM-powered solutions (prompt engineering, retrieval augmentation, multi-agent systems).
Demonstrated ability to design "human-in-the-loop" workflows and encode expert heuristics into governed, reusable logic (schemas, ontologies, taxonomies).
Experience working with domain experts to capture and structure domain knowledge.
Proven experience managing external technology or consulting partners delivering AI/ML or complex enterprise systems.
Strong product-thinking: transforming ambiguous problem spaces into shippable capabilities with measurable impact.
Comfort with CPG/retail analytics contexts (brand health, price/promotions, assortment, cannibalization, panel vs market measurement) or adjacent domains.
Experience with agent frameworks/tools, data access patterns, and integration (APIs) across analytics solutions.
Exceptional communication and change leadership—earning trust with Client Insights teams and senior client stakeholders.

Responsibilities

Architect & Orchestrate the AI Teammate
Own the end-state architecture and roadmap for domain centric AI application: multi-agent orchestration, tool/agent registry (e.g., data access, assortment modeling), retrieval augmentation, and guardrails.
Design interfaces and workflows so Client Insights users can delegate tasks (diagnose, recommend, forecast) and see evidence, steps, and data lineage (trust-by-design).
Operationalize "human-in-the-loop" training modes that expose rationale, artifacts, and gaps, enabling experts to correct/extend the system efficiently.
Capture & Model Domain Knowledge
Build the end-to-end mechanism to elicit, structure, and weight domain knowledge from experts—balancing industry best practice, company standards, and personal/role-level nuances.
Develop schemas/ontologies, taxonomies, and playbooks that encode diagnostic and recommendation patterns (e.g., brand health, price/promotions, assortment, equity).
Implement governance so contributions are validated, versioned, and weighted by authority (role, expertise, regulatory constraints).
Deliver Trusted Diagnostics & Recommendations
Stand up repeatable analyses for the most common client questions (e.g., "What's going on with my brand?") using our market data alongside learned heuristics.
Quantify actions via specialized agents (e.g., assortment optimization for SKU-level recommendations; pricing/trade scenarios) with transparent trade-offs and expected impact.
Enable strategic overlays (portfolio, pricing posture, innovation bets) so guidance reflects client-specific objectives—not just generic best practice.
Productize & Integrate Across the Portfolio
Partner with Product, Data Platform, and Solutions teams to package reusable capabilities into Liquid AI/Brainwaves and client-facing experiences.
Define APIs and integration patterns so agents and external data sources can be plugged in, discovered, and governed.
Evolve the user experience away from static forms towards natural-language workflows that pull the right data, tools, and logic on demand.
Lead, Influence & Change
Oversee vendor performance, ensuring quality, compliance, and alignment with internal teams.
Lead a matrixed squad of architects, applied scientists, and process/knowledge engineers; coach Insights SMEs on how to improve the AI teammate.
Set standards for evidence, explainability, and outcome-orientation ("sell more or operate more efficiently").
Drive adoption, training, and change-management across Client Insights and selected clients; measure impact and iterate.