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.
Across 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.
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Skills Required
- BS, BTech, or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or related quantitative field
- 8+ years overall IT experience
- 5+ years relevant experience in machine learning, statistical modeling, and AI solution development
- 2+ years experience with generative AI / large language models (LLMs)
- Proven track record designing, developing, and operationalizing AI/ML models at enterprise scale
- Strong proficiency in Python
- Proficiency with ML/AI frameworks (Scikit-learn, PyTorch, TensorFlow, Keras)
- Hands-on experience with LLMs, NLP, prompt engineering, embeddings, vector databases, and RAG
- Experience working with 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
State Street Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about State Street and has not been reviewed or approved by State Street.
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Retirement Support — Retirement support is framed as a standout component, highlighted by a 401(k) match described as 100% on the first 6% of base salary. This is positioned as a meaningful offset to less competitive cash compensation for some roles.
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Leave & Time Off Breadth — Leave and time off are portrayed as relatively robust, with references to multi-week vacation, paid holidays, sick time, and additional days tied to wellness or volunteering. This breadth is repeatedly treated as a tangible part of total rewards beyond base pay.
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Wellbeing & Lifestyle Benefits — Wellbeing and lifestyle benefits are presented as extensive, including the BeWell program, fitness discounts, onsite or supported health resources, and financial counseling. These offerings are depicted as strengthening the overall benefits proposition even when pay satisfaction is tepid.
State Street Insights
What We Do
At State Street, we partner with institutional investors all over the world to provide comprehensive financial services, including investment management, investment research and trading, and investment servicing. Whether you are an asset manager, asset owner, alternative asset manager, insurance company, pension fund or official institution, you can rely on us to be focused on your challenges. We are committed to doing what it takes to help you perform better — now and in the future
