Who we are looking for
We are seeking a highly motivated and technically skilled Data Scientist to join our AI/ML science team as part of the Agentic AI platform buildout. The Science pillar advances LLM research, fine‑tuning, evaluation frameworks, anomaly detection, and frontier model development—partnering closely with Platform Engineering, SRE, and Pods/Business Engagement to ship production-grade agents and platform features.
Why this role is important to us
This role will focus on applied research and product-oriented data science requirements – designing and validating domain-specific LLM solutions, building evaluation taxonomies and metrics and operationalizing fine-tuning/tooling for business use cases.
What you will be responsible for
Own BU specific problem statements and objectives to deliver value leveraging agentic AI workflows and domain LLMs
LLM research and fine-tuning including evaluating foundational models, fine-tuning and prompt optimization with robust data curation and governance alignment
Evaluation frameworks & taxonomies and Frontier model evaluation and buildout
Develop and maintain knowledge graphs and embeddings for semantic search and reasoning
Inference engine for scalable, low latency serving for tuned models, agentic workflows and cost optimization
Collaborate with engineering teams to deploy scalable AI solutions in production environments.
Conduct experiments and evaluations to benchmark model performance and ensure robustness
Opportunity to Research, document and socialize
Education & Preferred Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
3+ years of experience in applied machine learning or NLP.
Hands-on experience with LLM’s, GenAI or NLP at production scale
Strong understanding of graph databases (e.g., Neo4j, RDF, DGL) and knowledge graph construction.
Proficiency in Python and ML libraries (PyTorch, TensorFlow, Scikit-learn).
Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
Prior work on agentic AI is a big plus
Salary Range:
$90,000 - $157,500 AnnualThe range quoted above applies to the role in the location specified. If the candidate would ultimately work outside of the location above, the applicable range could differ.
Employees are eligible to participate in State Street’s comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans); and, eligibility for certain tax advantaged savings plans.
For a full overview, visit https://hrportal.ehr.com/statestreet/Home.
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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