Jobs
Benefits & Perks
•Healthcare
•401(k)
•Flexible Hours
•Remote Work
•Learning Budget
•Parental Leave
•Healthcare
•401k
•Flexible Hours
•Remote Work
•Learning
•Parental Leave
Required Skills
Python
Machine Learning
NLP
LLMs
PyTorch
TensorFlow
Scikit-learn
About the Role
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.
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.
Key Responsibilities
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Own BU specific problem statements and objectives to deliver value leveraging agentic AI workflows and domain LLMs
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LLM research and fine-tuning including evaluating foundational models, fine-tuning and prompt optimization with robust data curation and governance alignment
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Evaluation frameworks & taxonomies and Frontier model evaluation and buildout
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Develop and maintain knowledge graphs and embeddings for semantic search and reasoning
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Inference engine for scalable, low latency serving for tuned models, agentic workflows and cost optimization
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Collaborate with engineering teams to deploy scalable AI solutions in production environments.
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Conduct experiments and evaluations to benchmark model performance and ensure robustness
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Opportunity to Research, document and socialize
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Required Qualifications
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Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
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3+ years of experience in applied machine learning or NLP.
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Hands-on experience with LLM’s, GenAI or NLP at production scale
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Strong understanding of graph databases (e.g., Neo4j, RDF, DGL) and knowledge graph construction.
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Proficiency in Python and ML libraries (Py Torch, Tensor Flow, Scikit-learn).
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Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
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Prior work on agentic AI is a big plus
About State Street
What we do.State Street is one of the largest custodian banks, asset managers and asset intelligence companies in the world. From technology to product innovation, we’re making our mark on the financial services industry. For more than two centuries, we’ve been helping our clients safeguard and steward the investments of millions of people. We provide investment servicing, data & analytics, investment research & trading and investment management to institutional clients.
Work, Live and Grow.We make all efforts to create a great work environment. Our benefits packages are competitive and comprehensive. Details vary by location, but you may expect generous medical care, insurance and savings plans, among other perks. You’ll have access to flexible Work Programs to help you match your needs. And our wealth of development programs and educational support will help you reach your full potential.
Inclusion, Diversity and Social Responsibility. We truly believe our employees’ diverse backgrounds, experiences and perspectives are a powerful contributor to creating an inclusive environment where everyone can thrive and reach their maximum potential while adding value to both our organization and our clients. We warmly welcome candidates of diverse origin, background, ability, age, sexual orientation, gender identity and personality. Another fundamental value at State Street is active engagement with our communities around the world, both as a partner and a leader. You will have tools to help balance your professional and personal life, paid volunteer days, matching gift programs and access to employee networks that help you stay connected to what matters to you.
State Street is an equal opportunity and affirmative action employer.
About State Street
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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About State Street

State Street
PublicState Street Investment Management — formerly State Street Global Advisors (SSGA) — is an American investment management division of State Street Corporation founded in 1978 and the world's fourth largest asset manager, with nearly US$4.1 trillion in assets under management as of December 31, 2023.
10,001+
Employees
Boston
Headquarters
Reviews
3.5
6 reviews
Work Life Balance
2.5
Compensation
3.0
Culture
2.5
Career
3.2
Management
2.8
35%
Recommend to a Friend
Pros
More opportunity than other banks
Career advancement potential
Financial services experience
Cons
Slow hiring process
Poor Glassdoor reviews
Variance in firm quality opinions
Salary Ranges
76 data points
Junior/L3
Junior/L3 · Business Analyst
2 reports
$134,560
total / year
Base
$117,009
Stock
-
Bonus
-
$127,061
$142,060
Interview Experience
5 interviews
Difficulty
2.6
/ 5
Duration
21-35 weeks
Offer Rate
20%
Experience
Positive 20%
Neutral 60%
Negative 20%
Interview Process
1
Application Review
2
Recruiter Screen
3
Phone Interview
4
Technical/Hiring Manager Interview
5
Final Interview/Offer Stage
Common Questions
Technical Knowledge
Behavioral/STAR
Past Experience
Culture Fit
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