About The Position
The ALAC Decision Intelligence (DI) team at Apple is seeking a talented individual 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 involves both augmenting existing data solutions and innovating new data science projects, creating analytic experiences that simplify data into insights and catalyze decision-making. You will be key in leading and influencing teams on the translation of business problems and questions into data science models.
Requirements
- 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 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
- Strong time management skills with the ability to collaborate across multiple teams
- Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field
- Fluent in English and Portuguese
- 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
Nice To Haves
- Experience with Tableau Server, TabPy, and Extensions is a plus
- 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
- Advanced Degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field
- Spanish proficiency
Responsibilities
- 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
- 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
- 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
- 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
- Translate business problems into technical solutions and communicate findings to non-technical stakeholders
- Co-develop with data scientists and software engineers in production environments
- Balance competing priorities, long-term projects, and ad hoc requirements
- Present insights to senior leadership and influence business strategy
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What This Job Offers
Job Type
Full-time
Career Level
Senior
