Data Domain Expert
Apiphany
Data Domain Expert
Apiphany
AI-driven extraction from unstructured documents
We are looking for a Data Domain Specialist with prior industry experience who can help interpret and review client-provided mechanical and manufacturing-related data. This role acts as a bridge between client data and internal teams, ensuring the data being processed is relevant, accurate, and useful.
Key Responsibilities
Understand and review mechanical and manufacturing-related client data
Guide data processing teams on data relevance and usage
Help define tagging and classification guidelines
Review processed outputs and provide feedback for corrections
Support quality checks and validation activities
Assist during client discussions when domain input is required
Required Skills & Experience
3–6 years of experience in mechanical engineering/electrical engineering/electronics engineering roles within OEMs or manufacturing organizations
Strong understanding of mechanical and manufacturing concepts
Ability to interpret technical data and explain it clearly to non-domain teams
Experience with reviewing, validating, or tagging data
Nice to Have
Experience working with Engineering design docs, BOM, PLM Software and CAD models
Experience working with data, analytics, or AI teams
Exposure to quality, process, or documentation-heavy projects
Familiarity with tools used for tagging or reviewing data
What We’re Looking For
Strong domain fundamentals with a practical mindset
Ability to make judgment calls on data relevance
Good communication and collaboration skills
Comfortable working in a fast-moving startup environment
Apiphany provides an AI-powered platform that extracts key information from unstructured documents such as invoices, contracts, and reports. It uses artificial intelligence, machine learning, and natural language processing to understand document context and pull relevant data into structured formats, automating data entry and reducing errors. The system integrates with existing business workflows and continuously learns from new data to improve accuracy, while prioritizing security and regulatory compliance across industries. Its goal is to help businesses save time and cut costs by turning unstructured data into actionable, decision-support information.
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