Data Scientist, US Decision Intelligence

Apple•Cupertino, CA

About The Position

Apple’s US 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 a Data Scientist, US Decision Intelligence, 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, etc. This role will operate in both capacities, to augment existing data solutions, as well as innovate and trailblazing data science projects, crafting analytic experiences that simplify data into insights and catalyze decision-making.

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 (such as 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.
Ability 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.

Nice To Haves

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.

Responsibilities

Build and scale the automated insight pipeline that powers our sales organization.
Develop ML models that detect opportunities, diagnose performance issues, and recommend actions—then embed these insights into AI agents, dashboards, and GenAI-powered tools used by sales teams.
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.
Build RCA and recommendation engines that enhance summarization and chatbot capabilities.
Analyze agent interactions and implementing 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.
Act as a data translator, bridging the gap in expertise between technical teams, made up of data analysts, data engineers, software developers, and business stakeholders. Successfully bridging analytics and business, with the ability to speak the language of both.
Influence upstream data model design, drive KPI definitions, and develop your own data solutions as needed.