Summary
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 US 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, 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.
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.
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.
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).
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.
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.
