Lead Data Scientist - Assistant Vice President

Key Responsibilities

• Lead the end-to-end delivery of AI/ML and LLM-based solutions: problem framing, data exploration, feature engineering, model development, evaluation, deployment, and monitoring.
• Architect and implement advanced generative AI and agentic applications, including prompt engineering, fine-tuning, embedding, vector search, and retrieval-augmented generation (RAG).
• Define and drive best practices for model evaluation, benchmarking, error taxonomy, explainability, and anomaly detection.
• Build and maintain knowledge graphs, graph-based reasoning engines, and semantic search capabilities to power intelligent applications.
• Partner with engineering, SRE, and platform teams to productionize models, optimize for scalability and reliability, and integrate models into enterprise platforms and workflows.
• Ensure compliance with Responsible AI practices, model governance, regulatory requirements, and enterprise MRM controls.
• Drive the creation and adoption of reusable AI assets, experimentation frameworks, and MLOps/LLMOps pipelines.
• Communicate complex technical findings, recommendations, and risks to technical and non-technical stakeholders, including senior leadership.
• Mentor and guide junior and senior data scientists, fostering a culture of innovation, experimentation, and knowledge sharing.
• Stay current with advancements in LLMs, generative AI, agentic systems, and AI/ML engineering to drive pragmatic adoption within the organization.

Required Qualifications

• BS, BTech, or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field.
• 8+ years of overall experience in IT and 5+ years of relevant experience in machine learning, statistical modeling, and AI solution development, with 2+ years in generative AI/LLMs.
• Proven track record of designing, developing, and operationalizing AI/ML models at enterprise scale.
• Strong proficiency in Python and leading ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras, etc.).
• Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG.
• Experience with both structured and unstructured data, feature engineering, model experimentation, and tuning.
• Solid understanding of model validation, explainability, testing, monitoring, and Responsible AI.
• Proficiency with Azure and/or AWS for AI/ML development and deployment.
• Experience with graph databases and libraries (e.g., Neo4j, DGL), knowledge graphs, and semantic search.
• Strong stakeholder management, communication, and cross-functional collaboration skills.
• Demonstrated ability to work effectively across business, engineering, architecture, and governance teams.

Good to have skills

• Experience in financial services, risk, operations, or other regulated enterprise environments.
• Familiarity with model risk management, regulatory compliance, and Responsible AI controls.
• Experience with Databricks, Spark, SQL, LangChain, LlamaIndex, or similar AI/ML engineering toolsets.
• Knowledge of agentic AI, workflow orchestration, and enterprise AI platform capabilities.
• Experience with MLOps/LLMOps, CI/CD, model versioning, experiment tracking, and production monitoring.
• Prior mentoring or team leadership experience.
• Ability to present complex technical concepts in a business-friendly manner.

Work Schedule

On-premise

Keywords

AI/ML, Data Science, Generative AI, LLM, NLP, Predictive Modeling, Statistical Modeling, Python, PyTorch, TensorFlow, Scikit-learn, Azure, AWS, Databricks, Spark, RAG, Prompt Engineering, Vector Database, MLOps, LLMOps, Responsible AI, Model Governance, Explainability, Model Monitoring, Enterprise AI, Data Analytics

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