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Experis USA

Data Scientist, Dataset Assessment Lead

Sunnyvale, CA, United States

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Job details

Apply by: Jan 28, 2027
Experience: 5+ years of experience in:
Posting language: English
Posted: Oct 30, 2026

Original job description

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.

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More about this job

Skills

Supply chain management
Business processes
Change management
Data engineering
Prioritization
Decision making
Analytical skills
Process engineering
Documentation
Data quality assessment
Statistical quality control
SQL
Python
Reporting
Leadership
Data management
Forecasting
Statistics
Stakeholder management
Communication skills
Onboarding
Apache Hive
Databricks
Siemens Teamcenter
MES
Consumer electronics
Apply advanced manufacturing
ML
Cataloging
Google Analytics
Collaboration
Inventory management
Quality assurance procedures
Requirements gathering

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