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Latin America (ALAC) AIML Data Quality & Governance Scientist

Apple· Sales and Business Development
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Sao PauloFull-timePosted 4mo ago

About the role

AIML Data Quality & Governance Scientist for Apple’s ALAC AI/ML team focused on building enterprise knowledge graphs, ensuring data quality and governance, and enabling AI-driven sales insights across Latin America. Roles include business knowledge capture, knowledge graph development, data quality & governance, unstructured data architecture, AI enablement for sales, and cross-functional/global collaboration.

Responsibilities

  • Engage with business stakeholders to extract domain knowledge and translate into formal ontologies and knowledge graph schemas
  • Design, build, and evolve enterprise knowledge graphs aligned with global partner graphs and incorporate external data sources
  • Document and enforce data governance policies, establish data quality SLAs, dashboards, and leadership reporting
  • Propose and design ingestion pipelines for unstructured content and transform inputs into structured knowledge via embedding and classification
  • Design retrieval architectures (vector search, graph traversal) for AI tool consumption and evaluate AI agents against ALAC knowledge
  • Collaborate with global AI platforms and engineering teams to surface ALAC knowledge in shared AI tools and agents
  • Maintain user-centric focus to ensure sales teams receive fast, accurate, trustworthy answers
  • Collaborate with regional/global teams across analytics, AI platforms, technology infrastructure, sales operations, and channel management
  • Establish and communicate a roadmap for ALAC’s knowledge graph and data quality initiatives

Qualifications

  • 6+ years in Data Science, Knowledge Engineering, or related field with experience extracting complex business concepts into formal ontologies, schemas, or knowledge graphs
  • Hands-on experience designing/building knowledge graphs (e.g., Neo4j, GraphDB, Amazon Neptune) in production
  • Proficiency in graph query languages (Cypher, SPARQL, Gremlin), semantic standards (RDF, OWL, SHACL), and metadata tools (Alation, Collibra, DataHub)
  • Strong experience with data quality frameworks (rule definition, monitoring pipelines, anomaly detection, drift analysis)
  • Experience designing ingestion pipelines for unstructured data and ETL/integration workflows for structured data
  • 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
  • Bachelor’s degree in Computer Science, Information Science, Statistics, Engineering, or related field
  • Fluent in English and Portuguese

Skills

Required:Knowledge graph design and implementationGraph databases and query languagesSemantic standards and metadata managementData quality and governanceUnstructured data ingestionRetrieval architectures for AISQL and PythonEmbedding models and RAGCommunication and stakeholder management

Benefits

  • [Not specified in the description]
Last updated Jul 3, 2026. Interested? Apply on the company site.

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