Lead Data Scientist

About This Role

The Lead Data Scientist will drive business improvement and create a competitive advantage through innovative analytics in the Johnson & Johnson Innovative Medicine Supply Chain. This role involves applying cutting-edge AI/ML solutions to enhance data-driven decision-making and fostering innovation in a global environment.

Responsibilities

• Collaborate with cross-functional teams to understand business challenges and requirements, identify AI-driven opportunities, and ensure effective deployment of AI solutions.
• Experiment and implement cutting-edge AI/ML solutions (such as natural language processing, deep learning, predictive analytics, graph) to transform structured and unstructured data into business-critical insights.
• Continuously review and analyze academic research and industry publications, evaluate state-of-the-art AI/ML methodologies, and prototype innovative solutions to address real-world business problems.
• Evaluate and refine AI models to enhance accuracy, efficiency, trustfulness and business impact in decision-making processes.
• Clearly articulate methodologies, results, and insights to non-technical users and stakeholders, and present AI-driven recommendations to senior leadership to ensure strategic alignment and impact.

Requirements

• Advanced knowledge of traditional machine learning and deep learning foundations and algorithms, including classification, regression, clustering, transformer, reinforcement learning, and anomaly detection.
• Solid understanding of Natural Language Processing (NLP) techniques and Generative AI (GenAI) applications.
• Strong hands-on experience with Python and relevant packages (e.g., Transformers, LangChain, LangGraph, AG2, vLLM, pydantic, etc.).
• Excellent communication and presentation skills, with the ability to convey complex technical concepts to both technical and non-technical audiences.
• Excellent problem-solving skills and ability to work collaboratively in a team setting.

Qualifications

• Bachelor’s degree in statistics, applied mathematics, computer science, engineering, or a related quantitative discipline
• 4–6 years of industry experience solving business problems through the application of statistical modeling, machine learning, deep learning, generative AI, and Retrieval-Augmented Generation (RAG) techniques.

Nice to Have

• Deep understanding of agentic AI approaches, including the development of AI agents
• Exposure to containerization technologies (e.g., Docker) and cloud platforms (e.g., Azure) is a plus.
• Working knowledge of vector databases (e.g., Elasticsearch, Pinecone, FAISS, Weaviate) for information and knowledge retrieval.
• Strong familiarity with Git and version control best practices, with the ability to write clean, maintainable, and well-documented code.
• Solid understanding of RESTful API design principles and web service architecture.

Skills

* Required skills

Benefits