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Morgan Stanley

Data Scientist (NLP Specialist) – Legal and Compliance (Hybrid)

AI in this role

Design and deploy advanced NLP and GenAI models for compliance and financial crime surveillance at Morgan Stanley.

OpenAILangChainLangGraphCrewAISemantic KernelHugging FacePyTorchPythonJavaOpenai SdkHugging FaceGit
ragnlpmachine-learninglarge-language-modelsagentic-aiknowledge-graphsstatistical-modeling

We're seeking someone to join our team as a Data Scientist (NLP Specialist) in Non-Financial Risk to develop and deploy advanced AI models using NLP, Machine Learning, and quantitative techniques to improve surveillance and compliance monitoring.

In the Legal & Compliance division, we assist the Firm in achieving its business objectives by facilitating and overseeing the Firm's management of legal, regulatory, and franchise risk. This is a Director level position within the NFR Data & Analytics team, which is responsible for designing and optimizing surveillance models and tools using advanced analytical techniques to help identify suspicious and/or illegal behaviors like money laundering, market manipulation, insider trading, unfair sales or trading practices and other financial crimes.

Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.

What you'll do in the role:

  • Design high-performance systems leveraging cutting-edge techniques including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Agentic AI architecture, and knowledge graph analytics.

  • Create GenAI-based solutions to automate manual tasks and drive cost efficiency.

  • Conduct research to identify novel methods for enhancing analytical solutions.

  • Collaborate with stakeholders across Compliance, Legal, Financial Crimes, and IT to promote adoption of new AI/ML/NLP capabilities.

  • Champion innovative approaches to improve detection of suspicious activity.

What you'll bring to the role:

  • At least 4 years of hands-on experience building AI/ML/NLP solutions and applying advanced statistical methods to solve complex business problems, together with a Master's or PhD in a quantitative discipline such as Computer Science, Statistics, Mathematics, Engineering, or Machine Learning.

  • Knowledge of software design and system principles and excellent skills in either Python (preferred) or Java.

  • Experience with AI/ML/NLP software packages such as LangChain, LangGraph, Semantic Kernel, CrewAI, OpenAI SDK, PyTorch, HuggingFace, etc.

  • Experience in adhering to Software Development Life Cycle (SDLC) principles include GIT related operations.

  • Strong problem solving and time management skills.

  • Familiarity with financial markets, especially in Compliance, Non-Financial Risk, and Fraud analytics.

  • Experience with benchmark creation and evaluation including LLM-as-a-Judge based techniques.

  • Knowledge of Vector Stores, Linux, SPARQL, and Graph Databases.

All our positions are located in Montreal, Quebec. We offer a hybrid work environment, combining remote work and attendance in the office.

Knowledge of French and English is required.

WHAT YOU CAN EXPECT FROM MORGAN STANLEY:

At Morgan Stanley, we raise, manage and allocate capital for our clients – helping them reach their goals. We do it in a way that’s differentiated – and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.

To learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices​ into your browser.

Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.

Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.

For more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.

How we rate this

Data Scientist (NLP Specialist) – Legal and Compliance (Hybrid) at Morgan Stanley rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGNLPMachine learningLarge Language ModelsAgentic AIKnowledge GraphsStatistical ModelingOpenAI

Also common in data scientist jobs at this level

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. What NLP problem have you worked on, and how did you measure whether it actually worked?
  3. Tell me about a project where machine learning was part of your work. What did you do?
  4. Tell me about a project where large language models was part of your work. What did you do?
  5. Tell me about a project where agentic ai was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, NLP, Machine learning, Large Language Models, and Agentic AI. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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