Latin America (ALAC) AIML Data Quality & Governance Scientist

Apple Inc.; São Paulo; On-site; BRL 180,000 - 340,000; Full time

Job summary

Apple’s ALAC AI/ML team seeks an AIML Data Quality & Governance Scientist to translate business knowledge into enterprise knowledge graphs that support sales analytics and AI agents across Latin America.

You will lead data quality and governance, define ingestion architectures for structured and unstructured data, and ensure graphs align with global partner ecosystems while delivering measurable governance progress to leadership.

Qualifications

• 6+ years in Data Science, Knowledge Engineering, or related field translating concepts into ontologies or graphs.
• Hands-on experience building knowledge graphs in production with graph databases.
• Proficiency in graph query languages and semantic standards; metadata management tools.
• Strong data quality framework experience with rules, monitoring, drift analysis.
• Experience designing ingestion pipelines for unstructured data and ETL workflows.
• SQL, Python, and one major data platform (Snowflake/Spark/Hadoop) proficiency.
• Familiarity with vector similarity search, embedding models, and RAG architectures.
• Excellent communication and stakeholder management across regional/global teams.
• Bachelor’s degree in CS/IS/Stats/Engineering; fluent in English and Portuguese.

Responsibilities

• Engage with business stakeholders to extract domain expertise and translate into formal ontologies and graphs.
• Design and evolve enterprise knowledge graphs linked to global partner ecosystems.
• Incorporate external data sources with governance and traceability in the knowledge graph.
• Build and operate data quality monitoring pipelines and enforce governance policies.
• Map data lineage from raw sources to metrics and align with AI/BI tooling.

Skills

Graph databases

Knowledge graphs

Data governance

Data quality monitoring

SQL/Python

Bilingual English/Portuguese

Stakeholder management

Education

Bachelor's degree in Computer Science/Information Science/Statistics/Engineering

Advanced degree (MS or PhD) preferred

Tools

Neo4j

GraphDB

Amazon Neptune

Cypher

SPARQL

Gremlin

Alation/Collibra/DataHub

Job description

Latin America (ALAC) AIML Data Quality & Governance Scientist

Sao Paulo, Sao Paulo, Brazil Sales and Business Development

Apple's Latin America (ALAC) AI/ML team is looking for a rare combination: someone who is as comfortable discussing sales channel dynamics and business process logic as they are designing graph schemas and data pipelines.As an AIML Data Quality and Governance Scientist, your core work will be to listen deeply to business experts, extract and formalize what they know, and encode it into Enterprise Knowledge Graphs that become the trusted foundationfor AI agents, analytical tools, and decision-support systems used by sales teams across ALAC.This is not a role that works in isolation. The knowledge graphs you build must fit coherently into a broader, interconnected enterprise knowledge ecosystem — and your definitions, ontologies, and data models must be consistent with and linkable to graphs owned by partner teams, so that the sales user always gets a single, coherent, trustworthy picture.You will also lead data quality and governance efforts —translating the CoE Lead's governance strategy into documented, enforceable policies and operational standards across ALAC's data assets — and design ingestion architectures for unstructured, structured, and partner/channel/third-party data sources. Regular status and impact reporting to leadership on data quality trends and governance progress will be part of your ongoing rhythm.

Description

Business Knowledge Capture & Translation- Engage deeply with business stakeholders across sales, channel operations, and analytics —in close collaboration with the AI Business Process Champion— to extract domain expertise and bridge how the business thinks about its data with how AI systems need that knowledge structured- Translate business concepts, sales processes, channel structures, and operational logic into precise, formal ontologies and knowledge graph schemas- Codify metric definitions, calculation rules, and business hierarchies in a way that is unambiguous, governed, and accessible to both humans and AI systemsEnterprise Knowledge Graph Development- Design, build, and continuously evolve enterprise knowledge graphs that represent ALAC's business entities, data assets, metrics, channels, and processes in a machine-readable, AI-consumable form — maintaining backward compatibility and communicating schema changes to downstream consumers- Ensure knowledge models are architecturally consistent with and linkable to enterprise knowledge graphs owned by global partner teams — your graph is a regional node in a larger connected ecosystem- Incorporate partner, channel, and third-party data sources into the knowledge graph — ensuring external data is governed, traceable, and integrated with the same rigor as internal assets- Map end-to-end data lineage from raw source to derived metric, consuming the AI Business Process- Champion's process maps as primary inputs when modeling business flows and interdependenciesData Quality & Governance- Build and operationalize data quality monitoring pipelines — defining rules, anomaly detection, and drift analysis to surface issues before they reach AI outputs or sales-facing tools- Document and enforce data governance policies and procedures — translating the CoE Lead's governance strategy into operational rules, standards, and enforcement mechanisms, with quality checks embedded at ingestion, transformation, and serving layers- Optimize existing data processes and workflows for efficiency — actively improving how data flows through the organization, not just monitoring quality- Establish data quality SLAs, dashboards, and regular leadership reporting on governance progress, data quality trends, and alignment with the business roadmapUnstructured Data Architecture- Propose and design ingestion pipelines for unstructured content — market reports, channel briefings, operational documents — making them first-class citizens in the knowledge graph- Apply embedding models, entity extraction, and classification techniques to transform unstructured inputs into structured, linked knowledge — ensuring AI tools can reason with equal confidence over structured and unstructured sourcesAI Enablement for Sales Users- Design retrieval architectures — including vector search and graph traversal — aligned to the CoE Lead's solution architecture, and build evaluation pipelines to measure how accurately AI agents consume ALAC-specific knowledge- Collaborate with global AI platform and engineering teams to ensure ALAC knowledge is correctly integrated and surfaced in shared AI tools and agents- Keep the sales user experience as the north star — every knowledge modeling decision should make it easier for a sales team member to get a fast, accurate, trustworthy answerCross-functional & Global Collaboration- Collaborate actively with regional and global teams spanning analytics, AI platforms, technology infrastructure, sales operations, and channel management — navigating a complex, multi-stakeholder environment with clarity and credibility- Align ALAC knowledge models, ontology choices, and metric definitions with global standards — and influence upstream data model design so new data assets are born with the quality and semantic richness the knowledge graph requires- Establish and communicate a clear roadmap for ALAC's knowledge graph and data quality initiatives, managing expectations across regional and global stakeholders

Minimum Qualifications

• 6+ years in Data Science, Knowledge Engineering, or a related field — with demonstrated experience extracting complex business concepts and translating them into formal ontologies, schemas, or knowledge graph structures
• Hands-on experience designing and building knowledge graphs (e.g., Neo4j, GraphDB, Amazon Neptune) in a production environment
• Proficiency in graph query languages (Cypher, SPARQL, Gremlin), semantic standards (RDF, OWL, SHACL), and metadata management tools (e.g., Alation, Collibra, DataHub)
• Strong experience with data quality frameworks — rule definition, monitoring pipelines, anomaly detection, and drift analysis
• Experience designing ingestion pipelines for unstructured data as well as ETL/integration workflows for structured data across internal and external systems
• Proficiency in SQL, Python, and at least one major data platform (Snowflake, Spark, or Hadoop)
• Familiarity with vector similarity search, embedding models, and RAG architectures
• Strong communication and stakeholder management skills — comfortable influencing across regional and global teams without direct authority
• Bachelor's degree in Computer Science, Information Science, Statistics, Engineering, or a related field
• Fluent in English and Portuguese

Preferred Qualifications

• Experience working within a federated enterprise knowledge graph ecosystem — building graphs designed to interoperate with graphs owned by other teams
• Background in sales, channel operations, or business process domains
• Experience integrating knowledge graphs with LLM-based agents and RAG pipelines
• Familiarity with data governance frameworks (DAMA-DMBOK or equivalent) and experience managing governance projects from planning through execution
• Experience with partner, channel, or third-party data sets — ingestion, quality assessment, and integration into governed environments
• Experience leveraging AI/ML and automation to scale governance processes and improve data quality programmatically
• Advanced degree, MS or Ph.D., a plus

Apple is an equal opportunity employer that is committed to inclusion and diversity. Apple provides reasonable accommodations to applicants with disabilities and in accordance with local requirements. Apple is a drug-free workplace.

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