Head of Supply Chain

Shamrock AI; San Francisco (CA); On-site; USD 180,000 - 220,000; Full time

Benefits offered by this job

Early Stage Equity

Full medical, dental, vision

Location: San Francisco

Job summary

Shamrock AI is building an agentic data governance platform for large enterprises, helping AI agents safely operate across critical business processes. This role focuses on designing AI agents that understand BOM hierarchies, supplier data feeds, and demand signals to assess data quality, risk, and remediation needs rather than simply returning dashboards.

You will own the data governance framework, define scoring rubrics, prototype end-to-end solutions with LLMs, and lead enterprise deployment

Qualifications

• Deep experience in global supply chain—manufacturing, semiconductor, or high-tech OEM.
• Built and shipped AI agents using Claude or other LLMs; can write Python end-to-end.
• Data governance mindset with validation rules, failure modes, and risk scoring.
• Enterprise-scale program deployment including COE creation and stakeholder management.

Responsibilities

• Design and spec AI agents for data intelligence, including validation rule generation and data quality scoring.
• Translate data specifications into structured LLM contexts with grounding.
• Own the enterprise deployment playbook and COE rollout across geographies.
• Lead customer discovery workshops onsite to map data landscapes and risks.
• Draft the v1 product spec and go-to-market roadmap for early expansion.

Skills

Supply Chain Fluency

AI/LLM Practitioner

Data Governance

Enterprise Deployment

Python

Claude

OpenAI GPT

Tools

Claude

Python

OpenAI GPT

Job description

$180,000 - $220,000 USD + Early Stage Equity

About the Role

About the Role:

We’re building AI agents that give supply chain operators real-time intelligence about data quality, risk, and reliability - before bad data causes a costly downstream decision. These aren’t dashboards or BI wrappers. They’re agents that understand BOM hierarchies, supplier data feeds, and demand signals deeply enough to reason about failure modes, score data risk, and generate validation and remediation playbooks automatically.

The Problem You’ll Own:

Every large manufacturer depends on data from contract manufacturers, logistics partners, and internal systems - purchase orders, inventory records, demand forecasts, BOM structures. That data is almost always incomplete, inconsistently formatted, or wrong in ways invisible until someone makes a multi-million dollar decision on top of it.

The traditional answer is a validation team running SQL checks. The AI answer is an agent that reads the data specification, understands the business process it supports, identifies every field‑level failure mode, scores severity and risk, and writes the validation rule set and remediation playbook - automatically, at a depth and speed no analyst can match.

What You’ll Do:

• Design and spec AI agents for supply chain data intelligence: FMEA automation, validation rule generation, data quality scoring, anomaly detection, and remediation workflow design.
• Translate raw manufacturer and supplier data specifications, BOM governance documents, and data dictionaries into structured LLM context, few‑shot examples, and domain grounding.
• Own the data governance framework: define what good data looks like across supply chain domains and build the severity/occurrence/detectability rubrics agents use to prioritize problems
• Build working LLM prototypes: Use Claude/Lovable or similar tools to build working prototypes to gather feedback and communicate requirements.
• Run customer discovery workshops onsite: map data landscapes, identify highest‑risk data elements, return with a prioritized validation roadmap and working proof‑of‑concept.
• Build the enterprise deployment playbook: COE model, wave rollout approach, training materials, and executive narrative for scaling across business units and geographies.

What This Is Not:

• Not a data engineering role - you are building the intelligence layer, not the infrastructure beneath it.
• Not a pure PM role - you will build prototypes; if you haven’t yet spent time building with LLMs, this will be a stretch.
• Not a consulting engagement - you are building a product and a company, not delivering a project and moving on.
• Not for someone who needs a team beneath them - you are the first hire; the second hire reports to you.
• Not a role where domain knowledge substitutes for technical ability, or vice versa - both are required.

In Your First 90 Days:

• Days 1-30: Shadow 2-3 customer supply chain teams; document data feeds, failure modes, and manual workarounds; produce a prioritized problem inventory.
• Days 31-60: Ship a working AI agent that processes a real customer data spec, scores risk using FMEA logic, and outputs a structured validation playbook.
• Days 61-90: Write the product spec for v1, define the COE rollout model, identify the first three expansion accounts, present the go‑to‑market roadmap to the founding team.

Why Now:

Supply chain data governance has been an unsolved problem for decades. SQL rules don’t scale, don’t adapt, and don’t reason about business context. LLMs do. The companies that will own this market combine deep supply chain domain expertise with LLM‑native tooling. That combination is rare. We’re building it - and you are the person who makes it real.

Compensation & Logistics:

Salary: Competitive with Senior roles at early‑stage companies

Equity: Meaningful early‑stage equity grant - you are a foundational hire

Location: San Francisco; travel required for customer deployments (~15-25%)

Benefits: Full medical, dental, vision; learning budget

Requirements

This role exists at an intersection almost no one occupies. You need all four of these:

Supply Chain Fluency: Deep experience in global supply chain - manufacturing, semiconductor, or high‑tech OEM. You understand BOM hierarchies, supplier data feeds, excess and obsolete exposure, and why a wrong delivery commitment field causes chaos in a production plan.

AI / LLM Practitioner: Built and shipped AI agents using Claude or other LLMs. Can write Python end-to-end: read a spec, call an LLM, parse structured output, write results back. Resourceful and obsessively practical.

Data Governance Instinct: Think naturally in validation rules, failure modes, and risk scores. Familiar with FMEA methodology - SEV/OCC/DET scoring - applied to data quality

Enterprise Deployment: Deployed programs at enterprise scale. Know how to build a COE from scratch, manage cross-BU stakeholders, and write the executive deck that gets a VP to say yes

Background That Maps Well:

10-20 years of experience with meaningful time in supply chain operations or analytics, plus product management, consulting, or technical program management.

Hands‑on experience at a large enterprise - semiconductor, electronics manufacturing, or high‑tech OEM - dealing with manufacturer or supplier data quality problems firsthand.

Direct LLM experience (Anthropic Claude, OpenAI GPT, or similar) - professional, internal tooling, or serious personal project.

Data validation, quality scorecard, or governance framework design experience for structured enterprise data.

COE or program deployment experience: stood up a new capability, defined the operating model, managed the rollout.

FMEA methodology familiarity applied to data - SEV/OCC/DET scoring and RPN prioritization is a significant differentiator

About the Company

Shamrock is building an agentic assurance platform for large enterprises, helping companies safely deploy AI agents across critical business processes. Its platform creates an Enterprise Business Knowledge Graph connecting business processes, systems, data and configurations, giving AI agents the trusted context they need to operate reliably. The company's initial wedge is agentic test lifecycle automation for large ERP transformations, including SAP S/4HANA migrations. By continuously testing and validating enterprise processes, the platform builds an increasingly accurate and continuously updated representation of how the business operates - creating a foundation for data observability and autonomous business process automation.

We build the layer that:

• Prevent inconsistencies in data entry
• Detect inconsistencies across systems
• Validate business logic in real time
• Enable AI-driven workflows to run safely and reliably

We’re already live at a Fortune 10 Pharma Distributor and Fortune 20 AI Manufacturer. Our agents automate processes that power hundreds of billions of dollars in revenue.