About the role

AI summarised

Data Scientist role at Applied Materials, a semiconductor equipment leader, focusing on AI-driven solutions for supply chain and planning. The role involves developing and deploying machine learning models, including Generative AI and LLMs, to improve forecasting, planning accuracy, and operational decision-making.

EquipmentFull-time

Key Responsibilities

Collaborate with cross-functional teams to design and develop advance AI solutions for high value problems in supply chain and planning
Apply advanced statistical and machine learning techniques to extract insights and optimize planning, forecasting, and operational efficiency.
Build and maintain scalable data pipelines and analytical models using tools like Python, SQL, and cloud-native technologies.
Interface with internal stakeholders to gather requirements, define KPIs, and translate insights into actionable business strategies.
Lead the development and deployment of LLMs and Generative AI solutions tailored to supply chain and planning challenges
Experiment with cutting-edge techniques such as RAG, LoRA, and prompt engineering to build intelligent agents and decision-support systems.

Requirements

Masters Degree in Data Science, Computing, AI/ Analytics
7-10 years of deep expertise in machine learning, NLP, and transformer-based architectures.
Proficiency in Python, SQL, and ML frameworks (e.g., PyTorch, Hugging Face).
Familiarity with MLOps tools and cloud platforms (Azure preferred).
Strong understanding of supply chain, planning, and operational workflows.
Ability to align technical solutions with strategic business goals and KPIs.
May lead technical initiatives or mentor peers in AI experimentation and deployment.
Drives innovation by identifying new opportunities for generative AI across the organization.
Tackles complex, ambiguous problems using analytical thinking, experimentation, and domain knowledge.
Develops scalable solutions that adapt to evolving business needs.
Contributes directly to the success of AI initiatives that improve efficiency, accuracy, and agility in global operations.
Communicates technical concepts clearly to both technical and non-technical stakeholders.