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Job Title: Senior Data Scientist – Data & Analytics How You Will Do It Advanced Analytics, LLMs & Modeling Design and implement advanced machine learning models including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions (e.g., GPT, LLaMA, Claude). Develop use cases around enterprise search, document summarization, conversational AI, and automated knowledge retrieval using large language models. Fine-tune or prompt-engineer foundation models (e.g., OpenAI, Azure OpenAI, Hugging Face) for domain-specific applications. Evaluate and optimize LLM performance, latency, cost-effectiveness, and hallucination mitigation strategies for production use. Data Strategy & Engineering Collaboration Work closely with data and ML engineering teams to integrate LLM-powered applications into scalable, secure, and reliable pipelines. Contribute to the development of retrieval-augmented generation (RAG) architectures using vector databases (e.g., FAISS, Azure Cognitive Search). Support the deployment of models using MLOps principles, ensuring robust monitoring and lifecycle management. Business Impact & AI Strategy Partner with cross-functional stakeholders to identify opportunities for applying LLMs and generative AI to solve complex business challenges. Lead workshops or proofs-of-concept to demonstrate value of LLM use cases across business units. Translate complex model outputs, including those from LLMs, into clear insights and decision support tools for non-technical audiences. Thought Leadership & Mentorship Act as an internal thought leader on AI and LLM innovation, keeping JCI at the forefront of industry advancements. Mentor and upskill data science team members in advanced AI techniques, including transformer models and generative AI frameworks. Contribute to strategic roadmaps for generative AI and model governance within the enterprise. Qualifications & Experience Education in Data Science, Artificial Intelligence, Computer Science, or related quantitative discipline. 5+ years of hands-on experience in data science, including at least 1–2 years working with LLMs or generative AI technologies. Demonstrated success in deploying machine learning and NLP solutions at scale. Proven experience with cloud AI platforms—especially Azure OpenAI, Azure ML, Hugging Face, or AWS Bedrock. Technical Expertise Proficiency in Python and SQL, including libraries like Transformers (Hugging Face), LangChain, PyTorch, and TensorFlow. Experience with prompt engineering, fine-tuning, and LLM orchestration tools. Familiarity with data storage, retrieval systems, and vector databases. Strong understanding of model evaluation techniques for generative AI, including factuality, relevance, and toxicity metrics. Leadership & Soft Skills Strategic thinker with a strong ability to align AI initiatives to business goals. Excellent communication and storytelling skills, especially in articulating the value of LLMs and advanced analytics. Strong collaborator with a track record of influencing stakeholders across product, engineering, and executive teams. Preferred Qualifications Experience with IoT, edge analytics, or smart building systems. Familiarity with LLMOps, LangChain, Semantic Kernel, or similar orchestration frameworks. Knowledge of data privacy and governance considerations specific to LLM usage in enterprise environments. Show more Show less
