State Street
DataScientist–AgenticAI/Graph/LLMScience,AssistantVicePresident
Neural analysis suggests this role is
optimal for Mid candidates.
“Data Scientist – Agentic AI / Graph/ LLM Science, Assistant Vice President at State Street. Skills: Agentic AI, Graph/ LLM Science, LLM research, fine-tuning, evaluation frameworks, knowledge graphs, embeddings, Python, ML libraries. 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 al”
What You'll Achieve.
deliver value leveraging agentic AI workflows and domain LLMs; ship production-grade agents and platform features
Industry & Context.
Own BU specific problem statements and objectives to deliver value
What They're Looking For.
Must Have
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, understanding of graph databases (e. g. , Neo4j, RDF, DGL) and knowledge graph construction, Proficiency in Python, Proficiency in ML libraries (PyTorch, TensorFlow, Scikit-learn), Familiarity with cloud platforms (AWS, Azure, GCP), Familiarity with MLOps practices
Nice to Have
Prior work on agentic AI is a big plus
What You'll Do.
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
Conduct experiments and evaluations to benchmark model performance and ensure robustness
document and socialize
How You'll Work.
Team & Collaboration
partnering closely with Platform Engineering, SRE, and Podsusiness Engagement; Collaborate with engineering teams to deploy scalable AI solutions in production environments
Communication Scope
socialize
Full Job Description
**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, Sci
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