Role Overview

Reality Labs Supply Chain is establishing a next-generation NPI (New Product Introduction) data foundation to enable AI-driven supply chain capabilities across critical business processes, including:

• Product Change Management & PRQ Qualification
• Capacity Planning
• Capital Expenditure (CapEx) Planning
• In-Market Quality & Incident Management
• End-of-Manufacturing Planning

Before implementing AI and automation solutions, the organization requires a comprehensive understanding of the datasets that support these workstreams. This role will lead the assessment, inventory, and quality evaluation of those datasets, establishing a trusted baseline for future data engineering and AI initiatives.

The successful candidate will serve as the primary owner of dataset discovery, profiling, quality assessment, stakeholder interviews, and remediation prioritization. They will help determine which datasets are ready for AI-enabled use cases, identify gaps that could impact model performance and decision-making, and provide a clear roadmap for improving data readiness.

This role requires a blend of technical depth, supply chain domain expertise, strong analytical skills, and the ability to engage effectively with cross-functional stakeholders across engineering, operations, quality, and business teams.

Key Responsibilities

Dataset Inventory & Discovery

• Build and maintain the authoritative inventory of datasets supporting NPI and supply chain business processes.
• Document:
  • Source systems
  • Dataset owners
  • Refresh frequency and cadence
  • Data lineage and dependencies
  • Downstream consumers and business use cases
• Identify undocumented datasets and data flows that are actively supporting business operations.
• Establish a structured framework for dataset governance and ongoing inventory maintenance.

Stakeholder Assessment & Data Discovery

• Conduct structured interviews with:
  • Data owners
  • Operations Program Managers (OPMs)
  • Manufacturing Operations teams
  • Quality teams
  • Product and Process Engineering teams
• Assess how datasets are generated, maintained, consumed, and trusted in day-to-day decision-making.
• Capture business context that may not be reflected in existing documentation.
• Reconcile documented processes against actual operational usage.

Data Quality Assessment

• Evaluate each dataset against a formal seven-dimension data quality framework.
• Perform quantitative and qualitative assessments supported by documented evidence.
• Develop a consistent and defensible scoring methodology.
• Assess dimensions such as:
  • Accuracy
  • Completeness
  • Consistency
  • Timeliness
  • Validity
  • Uniqueness
  • Integrity
• Document findings, observations, risks, and recommendations for each dataset.
• Conduct statistical profiling and quality checks using SQL and Python.

Framework & Methodology Development

• Define and document:
  • Dataset intake templates
  • Assessment methodology
  • Data quality scoring rubric
  • Stakeholder interview guides
  • Evaluation criteria
• Create repeatable processes that can be leveraged after the engagement concludes.
• Establish standards that support future data governance and AI-readiness efforts.

Remediation Prioritization & Roadmap Development

• Deliver a prioritized remediation backlog identifying:
  • Datasets currently ready for AI and automation use cases
  • Datasets requiring remediation before use
  • Recommended improvement activities
  • Expected business impact of remediation efforts
• Categorize issues related to:
  • Data quality
  • Data availability
  • Governance
  • Ownership
  • Metadata management
  • Process inconsistencies
• Provide practical recommendations to accelerate AI adoption across supply chain workstreams.

Executive Reporting & Knowledge Transfer

• Prepare executive-level findings and recommendations for Supply Chain leadership.
• Communicate assessment results clearly to both technical and non-technical audiences.
• Deliver comprehensive handoff materials to the incoming Data Engineer and broader program team.
• Present key risks, opportunities, and priorities for future data foundation investments.

Required Qualifications

• 5+ years of experience in:
  • Data Science
  • Data Quality
  • Data Governance
  • Data Management and Analytics
• Demonstrated experience conducting enterprise-scale dataset inventories and data quality assessments.
• Strong understanding of supply chain, manufacturing, or NPI processes.
• Ability to evaluate and challenge datasets supporting:
  • Forecasting
  • Capacity Planning
  • Yield Management
  • Inventory Planning
  • Manufacturing Operations
• Advanced SQL expertise for data profiling and analysis.
• Strong Python skills, including experience with:
  • Pandas
  • Statistical analysis
  • Data profiling techniques
• Exceptional stakeholder management, interviewing, and facilitation skills.
• Strong written communication and executive presentation abilities.
• Proven ability to execute independently and deliver results within a fast-paced environment.
• Ability to complete a comprehensive assessment within an approximately 8-week execution window with minimal onboarding.

Preferred Qualifications

• Prior experience at Meta.
• Familiarity with Meta data platforms and analytics tools, including:
  • Hive
  • Presto
  • Databricks
  • Scuba
  • Unidash
• Experience working with manufacturing and engineering data sources, including:
  • Teamcenter
  • Factory MES systems
• Experience with:
  • Data Catalog platforms
  • Metadata Management solutions
  • Data Governance frameworks
• Knowledge of DAMA DMBOK or similar data quality and governance methodologies.
• Hardware NPI, consumer electronics, or advanced manufacturing industry experience.
• Experience supporting AI/ML readiness and enterprise data foundation initiatives.

Key Skills

Data Management & Quality

• Data Governance
• Data Quality Assessment
• Data Profiling
• Metadata Management
• Dataset Inventory
• Data Lineage
• Data Cataloging

Analytics & Technology

• SQL
• Python
• Pandas
• Statistical Analysis
• Data Engineering Collaboration
• Data Validation

Supply Chain & Manufacturing

• New Product Introduction (NPI)
• Manufacturing Operations
• Capacity Planning
• Yield Analysis
• Inventory Management
• Product Change Management
• Quality Management

Stakeholder Engagement

• Executive Communications
• Cross-Functional Collaboration
• Workshop Facilitation
• Interviewing & Discovery
• Requirements Gathering

Program Delivery

• Assessment Framework Design
• Roadmap Development
• Prioritization
• Documentation
• Executive Reporting

Ideal Candidate Profile

A highly analytical and business-savvy data professional who can bridge data science, governance, and supply chain operations. The ideal candidate has a track record of evaluating the health and usability of enterprise datasets, can quickly build credibility with manufacturing and NPI stakeholders, and is capable of translating complex data quality findings into actionable recommendations. They thrive in ambiguity, operate independently, and can establish the foundational data readiness required for successful AI and automation initiatives.