Latin America AIML Data Scientist - Decision Intelligence

Summary

Imagine what you could do here. The people here at Apple don’t just create products — they create the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it! Apple's ALAC Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating analytical solutions that have a direct and measurable impact on Apple Sales and its customers. As an ALAC DI Data Scientist, you will employ predictive modeling, data visualization, and statistical analysis techniques to build end-to-end solutions for internal stakeholders, leveraging sales performance data, market data, programs, external data, and more. This role will operate in both capacities — augmenting existing data solutions as well as innovating and trailblazing data science projects, crafting analytic experiences that simplify data into insights and catalyze decision-making. Analytics is a team sport and in your role, you will be key in leading and influencing teams on the translation of business problems and questions into data science models.

Minimum Qualifications

4+ years of experience in a Data Science, Data Analysis, or Data Visualization role Hands-on experience with LLMs, RAG architectures, and prompt engineering Strong proficiency in Python and ML/data science libraries Applied knowledge of statistical data analysis, predictive modeling, classification, Time Series techniques, sampling methods, multivariate analysis, hypothesis testing, and drift analysis Proficiency in SQL and experience with cloud data platforms (Snowflake, Spark, BigQuery, etc.) Expertise with data visualization tools (Tableau, d3, plotly, etc.) for data analysis and presentation. Experience with Tableau Server, TabPy, and Extensions is a plus Experience with Git and collaborative development workflows Familiarity with deployment frameworks and tools (Docker, Kubernetes, FastAPI, or similar) Comfort with ambiguity. Ability to structure complex analysis through data analysis and strategy research Proven ability to translate business problems into technical solutions and communicate findings to non- technical stakeholders Experience co-developing with data scientists and software engineers in production environments Strong time management skills with the ability to collaborate across multiple teams Able to balance competing priorities, long-term projects, and ad hoc requirements Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field Fluent in English and Portuguese

Preferred Qualifications

Production experience with GenAI frameworks (LangChain, LlamaIndex, Haystack, etc.) Familiarity with LLM observability and evaluation tools (LangSmith, Weights & Biases, TruLens, etc.) Experience with vector databases, embedding models, and retrieval algorithms Knowledge of agent architectures and knowledge graphs for LLM applications Experience with CI/CD pipelines and MLOps practices Experience with drift detection and model monitoring in production Track record of presenting insights to senior leadership and influencing business strategy Sound communication skills — adept at messaging domain and technical content at a level appropriate for the audience. Strong ability to gain trust with stakeholders and senior leadership Advanced Degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field Spanish proficiency

Description

Insight Pipeline & ML Development - Build and scale the automated insight pipeline that powers the ALAC sales organization — developing ML models that detect opportunities, diagnose performance issues, and recommend actions - Lead end-to-end insight development: from data preparation and statistical analysis to LLM prompt engineering that translates findings into sales-ready insights - Design and deploy ML models for forecasting, anomaly detection, attribution modeling, and causal inference — either building custom solutions or adapting Apple's existing ML services GENAI & Agent Integration - Embed insights into AI agents, dashboards, and GenAI-powered tools used by sales teams - Build RCA and recommendation engines that enhance summarization and chatbot capabilities - Analyze agent interactions and implement LLM evaluation pipelines to measure factual accuracy, latency, and user satisfaction Experimentation & Scaling - Support experimentation and A/B testing for new insight types and interaction methods - Partner with AI engineers and PMs to scale features across regions and tools - Influence upstream data model design, drive KPI definitions, and develop your own data solutions as needed Translation & Stakeholder Partnership - Act as a data translator, bridging the gap in expertise between technical teams — data analysts, data engineers, software developers — and business stakeholders, with the ability to speak the language of both