Latin America (ALAC) AIML Data Quality & Governance Scientist

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 foundation for 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 systems Enterprise 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
interdependencies Data 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 roadmap Unstructured 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 sources AI 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 answer Cross-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 Spanish proficiency Advanced degree, MS or Ph.D., a plus