Data Scientist - Business Process Re-Engineering

Austin, Texas, United States Operations and Supply Chain

Apple is where extraordinary people do their best work. If making a real impact excites you, a career here might be your dream — just be prepared to dream big.

Apple's growing supply chain complexity demands innovative approaches beyond traditional analytics. You'll join a team designing and developing advanced analytics solutions using GenAI, Agentic AI, and 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. If this sounds like you, we'd love to talk.

Engage with business teams to identify opportunities through in depth conversations and being able to translate those requirements into technical solutions and drive critical projects

Design and architect end-to-end data science solutions—selecting from established techniques or engineering novel algorithms tailored to complex supply chain business problems

Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models.

Continuously enhance and evolve deployed solutions through 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, 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

Develop custom models, algorithms, and 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 data science solutions

• PhD in Computer Science, 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
• Demonstrated experience in forecasting, optimization, or simulation within supply chain or operations domains
• Ability to work well in a fast-paced, iterative environment and deliver projects under timeline pressures
• Proven experience building and deploying large-scale data science and machine learning models, including anomaly detection, NLP, and deep learning techniques with MLOps practices, model versioning, and CI/CD pipelines for implementing, deploying and managing production AIML workflows and projects
• Experience prototyping and developing software in programming languages (Python, etc.) as well as leveraging advanced SQL for data manipulation
• Experience building out scalable solutions using GenAI technologies with an emphasis on Agentic solutions using MCP servers, agents, and skills
• Experience with data acquisition tools (e.g. SQL), data mining and data visualization. Strong background in AIML libraries and frameworks such as Scikit Learn, Tensor Flow, Py Torch
• Experience prototyping, developing software and implementing data science pipelines and applications in programming languages (Python/Java/C++)
• Track record of staying current with industry best practices, rapidly adopting emerging technologies (e.g., LLMs, RAG, vector databases), and building functional prototypes to validate concepts
• Champion a culture of experimentation and continuous learning, bringing innovative and strategic thinking to reporting, business analytics, and AI-powered automation
• Proven ability to own and deliver end-to-end projects from scoping through deployment and post-launch iteration
• Proficiency with cloud data platforms (e.g., Snowflake), relational databases (e.g., MySQL), interactive front-end frameworks (e.g., Streamlit, Tableau, Thought Spot), and containerization/orchestration tools (Docker,…