Principal Data Scientist, Vice President

Role Description 

We are seeking a visionary and accomplished Data Scientist at the Vice President (VP) level to lead our enterprise AI/ML and generative AI initiatives. As a senior member of the organization, you will set the strategic direction for LLM and generative AI research, model development, and productionization, partnering with business units, platform engineering, and governance to deliver innovative, secure, and scalable AI solutions.  

This role requires deep technical expertise in data science and AI, a proven track record in deploying AI at scale, and the ability to influence and guide the organization’s AI strategy and best practices. 

Key Responsibilities 

• Define and drive the strategic roadmap for AI/ML and generative AI (LLM) adoption across the enterprise, aligning with business objectives. 

• Lead and oversee the design, development, deployment, and lifecycle management of advanced ML, NLP, and generative AI solutions for high-value, complex business problems. 

• Architect and operationalize large-scale LLM-based solutions, including prompt engineering, fine-tuning, embeddings, vector search, RAG, and knowledge graph integration. 

• Establish and own enterprise-wide frameworks for model evaluation, benchmarking, error taxonomy, explainability, anomaly detection, and Responsible AI. 

• Guide cross-functional teams in productionizing AI models, integrating them with enterprise platforms, and ensuring robust MLOps/LLMOps practices. 

• Ensure compliance with model governance, regulatory, and Responsible AI standards, collaborating with risk and compliance teams. 

• Build and scale high-performing data science teams, mentoring and developing talent, and fostering a culture of innovation and excellence. 

• Partner with senior business leaders, product owners, engineering, architecture, and governance to translate strategy into actionable AI roadmaps. 

• Advocate for the adoption of reusable AI assets, experimentation frameworks, and best practices for model development, deployment, and monitoring. 

• Stay at the forefront of advancements in AI/ML, LLMs, agentic systems, and enterprise AI platforms, and drive pragmatic adoption of emerging capabilities. 

• Represent the organization in AI/ML vendor selection, external partnerships, and industry forums. 

Required Qualifications  

• Bachelor’s, BTech, or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative discipline. 

• 12-17 years of overall experience in IT and 8+ years of relevant experience in data science, machine learning, and advanced analytics, including 2+ years leading LLM/gen AI solution development and deployment at enterprise scale. 

• Proven success in designing, developing, and operationalizing AI/ML models for large organizations. 

• Deep technical proficiency in Python and major AI/ML frameworks (PyTorch, TensorFlow, Scikit-learn, Keras, etc.). 

• Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and retrieval-augmented generation (RAG). 

• Experience with structured and unstructured data, knowledge graphs, feature engineering, and graph-based reasoning. 

• Strong understanding of model validation, explainability, testing, monitoring, Responsible AI, and model risk management. 

• Expertise in Azure and/or AWS for AI/ML development, deployment, and MLOps/LLMOps practices. 

• Demonstrated leadership experience building and managing high-performing data science teams. 

• Excellent communication, stakeholder management, and influencing skills, with the ability to present complex technical concepts to executive leadership. 

Good to have skills 

• Experience in financial services, risk, operations, investment analytics, or other regulated enterprise domains. 

• Familiarity with Databricks, Spark, SQL, LangChain, LlamaIndex, or similar engineering toolsets. 

• Exposure to agentic AI, workflow orchestration, and advanced enterprise AI platforms. 

• Experience with CI/CD pipelines, model versioning, experiment tracking, and production monitoring. 

• Active participation in AI/ML industry forums, standards bodies, or academic collaborations.