Senior Data Scientist – Data & Analytics

This role focuses on leveraging advanced analytics, Large Language Models (LLMs), and sophisticated modeling techniques to drive innovation and solve complex business challenges. The Senior Data Scientist will design and implement cutting-edge machine learning models, including deep learning, time-series forecasting, recommendation engines, and LLM-based solutions like GPT, LLaMA, and Claude. Key responsibilities include developing use cases for enterprise search, document summarization, conversational AI, and automated knowledge retrieval using LLMs. The role also involves fine-tuning and prompt-engineering foundation models (e.g., OpenAI, Azure OpenAI, Hugging Face) for specific applications and optimizing LLM performance, latency, cost, and hallucination mitigation for production environments.

Collaboration with data and ML engineering teams is crucial for integrating LLM-powered applications into scalable, secure, and reliable pipelines. Contributions will extend to developing retrieval-augmented generation (RAG) architectures using vector databases such as FAISS and Azure Cognitive Search, and supporting model deployment through MLOps principles with robust monitoring and lifecycle management.

The Senior Data Scientist will partner with cross-functional stakeholders to identify opportunities for applying LLMs and generative AI to address business needs. This includes leading workshops and proofs-of-concept to demonstrate the value of LLM use cases across various business units. Translating complex model outputs, including those from LLMs, into clear insights and decision support tools for non-technical audiences is a key aspect of the role.

As an internal thought leader, the Senior Data Scientist will champion AI and LLM innovation, keeping the company at the forefront of industry advancements. Mentoring and upskilling data science team members in advanced AI techniques, including transformer models and generative AI frameworks, will be a significant responsibility. Contributions to strategic roadmaps for generative AI and model governance within the enterprise are also expected.

Qualifications & Experience

• Education in Data Science, Artificial Intelligence, Computer Science, or a related quantitative discipline.
• 5+ years of hands-on experience in data science, with at least 1-2 years specifically working with LLMs or generative AI technologies.
• Demonstrated success in deploying machine learning and Natural Language Processing (NLP) solutions at scale.
• Proven experience with cloud AI platforms, particularly Azure OpenAI, Azure ML, Hugging Face, or AWS Bedrock.

Technical Expertise

• Proficiency in Python and SQL, including libraries such as 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 metrics for factuality, relevance, and toxicity.

Leadership & Soft Skills

• Strategic thinking with a strong ability to align AI initiatives with business goals.
• Excellent communication and storytelling skills, particularly in articulating the value of LLMs and advanced analytics.
• Strong collaboration skills 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.