LLM Data Scientist, AVP
State Street
LLM Data Scientist, AVP
Job Description
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. About State StreetAcross the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.Discover more information on jobs at StateStreet.com/careersRead our CEO Statement
Locations
- India, Bangalore
Skills Required
- Pythonintermediate
- LLMsintermediate
- both structuredintermediate
- Azure and/or AWS for AI/ML developmentintermediate
- graph databasesintermediate
- financial servicesintermediate
- model risk managementintermediate
- Databricksintermediate
- agentic AIintermediate
- MLOps/LLMOpsintermediate
Required Qualifications
- BS, BTech, or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field. (degree in master)
- BS, BTech, or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related quantitative field. (degree in master)
- 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. (experience, 8 years)
- 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. (experience, 8 years)
- Proven track record of designing, developing, and operationalizing AI/ML models at enterprise scale. (experience)
- Proven track record of designing, developing, and operationalizing AI/ML models at enterprise scale. (experience)
- Strong proficiency in Python and leading ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras, etc.). (experience)
- Strong proficiency in Python and leading ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras, etc.). (experience)
- Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG. (experience)
- Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG. (experience)
- Experience with both structured and unstructured data, feature engineering, model experimentation, and tuning. (experience)
- Experience with both structured and unstructured data, feature engineering, model experimentation, and tuning. (experience)
- Solid understanding of model validation, explainability, testing, monitoring, and Responsible AI. (experience)
- Solid understanding of model validation, explainability, testing, monitoring, and Responsible AI. (experience)
- Proficiency with Azure and/or AWS for AI/ML development and deployment. (experience)
- Proficiency with Azure and/or AWS for AI/ML development and deployment. (experience)
- Experience with graph databases and libraries (e.g., Neo4j, DGL), knowledge graphs, and semantic search. (experience)
- Experience with graph databases and libraries (e.g., Neo4j, DGL), knowledge graphs, and semantic search. (experience)
- Strong stakeholder management, communication, and cross-functional collaboration skills. (experience)
- Strong stakeholder management, communication, and cross-functional collaboration skills. (experience)
- Demonstrated ability to work effectively across business, engineering, architecture, and governance teams. (experience)
- Demonstrated ability to work effectively across business, engineering, architecture, and governance teams. (experience)
Preferred Qualifications
- Experience in financial services, risk, operations, or other regulated enterprise environments. (experience)
- Experience in financial services, risk, operations, or other regulated enterprise environments. (experience)
- Familiarity with model risk management, regulatory compliance, and Responsible AI controls. (experience)
- Familiarity with model risk management, regulatory compliance, and Responsible AI controls. (experience)
- Experience with Databricks, Spark, SQL, LangChain, LlamaIndex, or similar AI/ML engineering toolsets. (experience)
- Experience with Databricks, Spark, SQL, LangChain, LlamaIndex, or similar AI/ML engineering toolsets. (experience)
- Knowledge of agentic AI, workflow orchestration, and enterprise AI platform capabilities. (experience)
- Knowledge of agentic AI, workflow orchestration, and enterprise AI platform capabilities. (experience)
- Experience with MLOps/LLMOps, CI/CD, model versioning, experiment tracking, and production monitoring. (experience)
- Experience with MLOps/LLMOps, CI/CD, model versioning, experiment tracking, and production monitoring. (experience)
- Prior mentoring or team leadership experience. (experience)
- Prior mentoring or team leadership experience. (experience)
- Ability to present complex technical concepts in a business-friendly manner. (experience)
- Ability to present complex technical concepts in a business-friendly manner. (experience)
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.
- 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).
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Stay current with advancements in LLMs, generative AI, agentic systems, and AI/ML engineering to drive pragmatic adoption within the organization.
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