AVP – Lead Data Scientist

About the Role

We are seeking an accomplished and hands-on Lead Data Scientist (AVP) to join our AI/ML Science team as part of the Agentic AI platform initiative. This role is pivotal in shaping the science strategy for large language models (LLMs), generative AI, and advanced analytics across business domains. You will be responsible for leading the design, development, evaluation, and operationalization of AI/ML solutions that address complex, high-impact business problems at enterprise scale.

The ideal candidate will bring deep technical expertise in machine learning, generative AI, advanced analytics, and model lifecycle management, as well as proven experience in translating business needs into robust, secure, and scalable AI solutions. You will collaborate closely with business stakeholders, engineering, architecture, and governance teams, and play a key role in mentoring data scientists and advancing best practices in model governance and Responsible AI.

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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