Principal Data Scientist

Experience: 10–15+ years in Data Science & Applied ML, with 4+ years dedicated to Generative AI, LLMs, and Advanced Retrieval Systems Primary Objective : We are seeking a Principal Data Scientist specializing in Generative AI to serve as our top technical authority on model science, advanced retrieval methodologies, AI quality, and evaluation strategy. In this leadership role, you will define the scientific vision for our enterprise GenAI capabilities—pioneering novel RAG architectures (GraphRAG, Hybrid Search), designing org-wide evaluation and alignment frameworks, leading domain-specific model adaptation (SFT, DPO/RLHF), and ensuring our production AI systems maintain world‑class accuracy, safety, and reliability. Success looks like: Defining company‑wide evaluation benchmarks that eliminate hallucination risks, championing state‑of‑the‑art retrieval/fine‑tuning techniques that give us a competitive edge, and bridging the gap between cutting‑edge AI research and production‑grade business value across global delivery teams. Key Responsibilities Scientific Leadership & Strategic Vision Define the enterprise science roadmap for Generative AI, model alignment, advanced retrieval, and AI safety/trustworthiness. Pioneer state‑of‑the‑art RAG paradigms—including GraphRAG, Knowledge Graphs, multi‑modal embeddings, and hybrid sparse/dense search architectures. Lead model selection, adaptation, and optimization strategies across proprietary (OpenAI, Claude, Gemini) and open‑source foundation models (Llama, Mistral, Qwen), evaluating when to use RAG vs. Fine‑tuning (SFT/LoRA) vs. In‑Context Learning. Partner with business executives and product leaders to translate complex business objectives into quantifiable AI metrics and scientific experiments. AI Quality, Evaluation & Trust Blueprinting Establish enterprise‑wide evaluation standards, benchmarking frameworks, and automated testbeds for hallucination detection, factual precision, bias mitigation, and toxicity prevention. Architect continuous monitoring and statistical evaluation mechanisms for live production systems (online LLM observability, output drift, and semantic regression testing). Lead Responsible AI, Explainability, and AI Governance initiatives—defining scientific methodologies to audit AI decisions in highly regulated enterprise environments. Cross‑Functional Collaboration & Technical Mentorship Work in lockstep with Enterprise AI Architects and AI Engineering teams to seamlessly transition research prototypes into scalable, production‑ready microservices. Mentor and upskill senior data scientists, applied ML engineers, and offshore team members on advanced ML methods, statistical evaluation rigor, and research best practices. Represent the enterprise as a subject matter expert in AI innovation, presenting key technical breakthroughs to executive stakeholders. Technical Experience & Qualifications: 10–15+ years of progressive experience in Data Science, Machine Learning, and Applied Research, with 4+ years leading Generative AI & Deep Learning initiatives. Deep theoretical and practical mastery of LLM architectures, attention mechanisms, embedding spaces, fine‑tuning techniques (PEFT, LoRA, QLoRA), and model alignment (DPO, RLHF). Proven track record designing enterprise‑grade RAG architectures, vector search ecosystems (Pinecone, Qdrant, OpenSearch, pgvector), and hybrid retrieval algorithms. Industry expertise in designing automated LLM evaluation frameworks (e.g., RAGAS, DeepEval, TruLens) and ground‑truth dataset curation methods. Deep fluency in Python and ML/DL frameworks (PyTorch, Scikit‑learn, Pandas, NumPy). Experience deploying and optimizing models across cloud platforms (Azure AI Foundry, AWS Bedrock, Databricks/MLflow). Preferred Skills: Hands‑on expertise with GraphRAG, Knowledge Graphs, and graph databases (Neo4j). Strong background in Banking, Financial Services, or similarly regulated enterprise domains. Experience with distributed computing frameworks (PySpark, Databricks) for large‑scale data processing and model evaluation. Publication record or open‑source contributions in applied NLP, Information Retrieval, or Generative AI. Core Competencies & Soft Skills Scientific Rigor: Unwavering commitment to statistical validation, empirical testing, and evidence‑based decision making. Business Acumen: Ability to tie complex technical metrics (e.g., Normalized Discounted Cumulative Gain, BLEU/ROUGE, context precision) directly to ROI and business KPIs. Thought Leadership: Clear, persuasive communication style capable of building consensus between research scientists, platform engineers, and business leaders. #J-18808-Ljbffr