Data Scientist - Business Process Re-Engineering

Summary

Apple is where extraordinary people do their best work. If making a real impact excites you, a career here might be your dream. Apple's growing supply chain complexity demands innovative approaches beyond traditional analytics. You'll join a team designing and building functional GenAI/LLM-powered applications and agent workflows, alongside modern data science methods, to drive decisions. You're passionate about turning data into impactful insights, staying ahead of technology trends, and thrive navigating ambiguity in a fast-paced environment.

Minimum Qualifications

PhD in Computer Science, Computer Software Engineering, Statistics, Applied Math, Data Science, Operations Research or a related field and 5+ years of industry experience OR MS in related field with 8+ years hands-on industry experience Strong software engineering fundamentals (data structures, algorithms, system design) with production-quality coding in Python and TypeScript/JavaScript and advanced SQL, following best practices in version control (Git), code review, testing, and documentation Experience developing end-to-end applications, including backend services and RESTful APIs (e.g., FastAPI) and interactive front-ends (e.g., React, Streamlit) Proven experience building and deploying production GenAI/LLM-powered applications and machine learning models on cloud platforms (e.g., AWS, GCP, Azure), with MLOps practices, model versioning, CI/CD pipelines, containerization/orchestration (Docker, Kubernetes), and monitoring to deliver scalable, reliable services Hands-on experience integrating LLM APIs (e.g., OpenAI, Anthropic) and frameworks (e.g., LangChain, LangGraph) to build scalable agentic solutions using MCP servers, agents, and skills Expertise in software engineering and designing evals to measure LLM output quality, accuracy, and reliability Strong background in AIML libraries (e.g., Scikit-learn, TensorFlow, PyTorch) and predictive modeling techniques, including classification, regression, clustering, and anomaly detection Demonstrated experience in forecasting, optimization, or simulation within supply chain or operations domains Proven ability to own projects end-to-end, from scoping and rapid prototyping through deployment and post-launch iteration, while staying current with emerging technologies (e.g., new foundation models, agent frameworks)

Preferred Qualifications

Experience designing and maintaining scalable data pipelines (ETL/ELT, batch and streaming; e.g., Airflow, Spark, dbt) on cloud data platforms (e.g., Snowflake) and relational databases (e.g., MySQL), with meticulous attention to data quality and integrity, and surfacing insights through BI tools (e.g., Tableau, ThoughtSpot) Experience building Retrieval-Augmented Generation (RAG) and vector search solutions using vector databases Experience adapting and orchestrating pre-trained foundation models (e.g., tool use, multi-agent orchestration) Exceptional ability to translate complex AI/ML concepts into clear business narratives and influence senior leadership, with strong collaboration skills to partner across functions and integrate diverse feedback Self-sufficient, intellectually curious, and biased toward action, thriving amid autonomy, ambiguity, and timeline pressure in a fast-paced, iterative environment

Description

Design and architect end-to-end GenAI/LLM-powered applications and agent workflows, adapting and orchestrating pre-trained foundation models, tailored to complex supply chain business problems Engage with business teams to identify opportunities through conversations and translating requirements into technical solutions to drive critical projects Collaborate with data engineers and infrastructure partners to integrate LLM APIs and frameworks (e.g., LangChain, LangGraph) and operationalize robust solutions Build production quality solutions or enhance and evolve deployed solutions through engineering, evals, monitoring, feedback loops, and iteration to meet changing business needs with agility Present key findings to leadership to evaluate business impact, in non-technical terms Research and evaluate emerging technologies, including GenAI, agentic frameworks, software engineering, and advanced visualization tools, to expand the team's technical capabilities and accelerate innovation Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation Build RAG pipelines and vector search capabilities, along with interactive visualizations, including dashboards and self-service tools, to deliver actionable Supply Chain insights at scale Wrangle and analyze data to identify patterns, trends, and feature engineering Define and track key performance metrics to quantify the business value of deployed AI solutions