Lead Data Scientist

Johnson & Johnson Innovative Medicine Supply Chain (IMSC) Data Science and AI group is seeking a skilled and motivated Lead Data Scientist to play a key role in our transformation journey. In this position, you will apply innovative analytics to drive business improvement and create a competitive advantage, enhancing data-driven decision-making across the supply chain and beyond. The ideal candidate will work closely with business stakeholders to understand requirements, explore and experiment with cutting-edge AI/ML solutions, and clearly communicate methodologies and results to both technical and non-technical audiences.

Key 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, and 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.

Education and Experience:

· Bachelor’s degree in statistics, applied mathematics, computer science, engineering, or a related quantitative discipline is required.

· Master’s or PhD in a quantitative field such as statistics, applied mathematics, computer science, engineering, or a related discipline from an accredited college or university is preferred.

· 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.

Qualifications:

· 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.

Preferred Qualifications:

· 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.

Other

· May require up to 10% of domestic and international travel

Preferred Skills:

Advanced Analytics, Business Intelligence (BI), Coaching, Collaborating, Critical Thinking, Data Analysis, Database Management, Data Privacy Standards, Data Reporting, Data Savvy, Data Science, Data Visualization, Econometric Models, Process Improvements, Technical Credibility, Technologically Savvy, Workflow Analysis

Hybrid Work