Data Scientist

Key Responsibilities:

This role focuses on strengthening and scaling current AI-driven solutions in supply chain and planning, with an emphasis on delivering measurable business impact through advanced analytics and intelligent systems. The Data Scientist will work on high-impact projects involving structured and unstructured data, statistical modeling, and machine learning to enhance forecasting, planning accuracy, and operational decision-making. In addition to supporting today’s priorities, the role will contribute to the team’s long-term vision by exploring emerging AI technologies, including Generative AI and Large Language Models (LLMs), to ensure readiness for future innovation. 

• 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.