Apple is seeking a Data Scientist to design and architect end-to-end data science solutions for complex supply chain business problems. The role applies GenAI and agentic AI alongside modern data science methods, partnering with business teams and operationalizing models for production use.
Key Responsibilities
- Partner with business teams through in-depth conversations to identify opportunities, translate requirements into technical solutions, and drive critical projects.
- Design and architect end-to-end data science solutions by selecting established approaches or engineering novel algorithms for complex supply chain challenges.
- Collaborate with data engineers and infrastructure partners to implement robust solutions and operationalize models.
- Improve deployed solutions over time through monitoring, feedback loops, and iterative refinement to meet evolving business needs.
- Present key findings to leadership, communicating impact in non-technical terms.
- Research and evaluate emerging technologies, including GenAI, agentic frameworks, and advanced visualization tools, to expand technical capabilities and accelerate innovation.
- Promote a culture of experimentation and continuous learning across reporting, business analytics, and AI-powered automation.
- Build 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 uncover patterns, trends, and features through feature engineering.
- Define and track key performance metrics to quantify the business value of deployed data science solutions.
Minimum Requirements
- PhD in Computer Science, Statistics, Applied Math, Data Science, Operations Research, or a related field with 5+ years of industry experience, or MS in a related field with 8+ years of hands-on industry experience.
- Demonstrated experience in forecasting, optimization, or simulation within supply chain or operations domains.
- Ability to work effectively in a fast-paced, iterative environment and deliver projects under timeline pressure.
- Proven experience building and deploying large-scale data science and machine learning models, including anomaly detection, NLP, and deep learning, with MLOps practices such as model versioning and CI/CD pipelines for production AI/ML workflows.
- Experience prototyping software in programming languages such as Python, along with advanced SQL for data manipulation.
- Experience building scalable solutions using GenAI technologies, with emphasis on agentic solutions using MCP servers, agents, and skills.
- Experience with data acquisition tools (for example, SQL), data mining, and data visualization, plus strong background in AIML libraries and frameworks including Scikit-Learn, TensorFlow, and PyTorch.
- Experience prototyping and implementing data science pipelines and applications in programming languages such as Python, Java, or C++.
- Track record of staying current with industry best practices, rapidly adopting emerging technologies such as LLMs, RAG, and vector databases, and building functional prototypes to validate concepts.
- Demonstrated ability to own and deliver end-to-end projects from scoping through deployment and post-launch iteration.
- Proficiency with cloud data platforms such as Snowflake, relational databases such as MySQL, interactive front-end frameworks such as Streamlit, Tableau, and ThoughtSpot, and containerization or orchestration tools such as Docker and Kubernetes.
- Working knowledge of predictive modeling and classification algorithms, regression, clustering, and anomaly detection.
- Strong ability to translate complex AI and ML concepts into clear business narratives, with demonstrated talent for data storytelling and influencing senior leadership and cross-functional partners through analysis presentation.
- Self-sufficient work style to thrive in an environment of autonomy amid ambiguity.
- Strong interpersonal and collaboration skills to partner across functions, share knowledge, communicate findings, and incorporate diverse feedback.
Preferred Qualifications
- Meticulous attention to detail, data integrity, and data wrangling.
- Experience delivering end-to-end projects and getting things done.
- High intellectual curiosity to learn and understand business needs.
- Self-sufficient ability to thrive in an autonomy-driven environment amid ambiguity.
- Strong interpersonal and collaboration skills to partner effectively across functions, share knowledge, communicate findings, and integrate diverse feedback.
Technologies
GenAI, Agentic AI, MCP servers, agents, skills, Python, SQL, Scikit Learn, TensorFlow, PyTorch, NLP, deep learning, MLOps, model versioning, CI/CD, LLMs, RAG, vector databases, Snowflake, MySQL, Streamlit, Tableau, ThoughtSpot, Docker, Kubernetes, Java, C++.
