Data Scientist, US Decision Intelligence

Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish.

Apple's Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers.

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.

Description

In this role, you will build and scale the automated insight pipeline that powers our sales organization. You'll 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.

Your core responsibilities include:

- 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.

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.

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 (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).