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Job Description & How to Apply Below
Location: Montreal
Join to apply for the Data Scientist (Hybrid) role at Morgan Stanley .
We’re seeking someone to join our team as an Associate in the Non-Financial Risk (NFR) department to support the Financial Crime team. The role involves maintaining, developing, designing, and optimizing surveillance models, approaches, and tools using advanced analytical techniques such as supervised and unsupervised machine learning, and evolving techniques like graph analytics. These surveillance tools help identify suspicious or illegal behaviors such as money laundering, market manipulation, insider trading, unfair sales or trading practices, and other financial crimes.
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 an Associate level position within the Data and Analytics department to support the team as they develop and maintain tooling for the wider Privacy NFR team.
What You’ll Do In
The Role
Develop and implement advanced data analytics solutions for financial crimes detection and prevention.
Use graph-based techniques and graph analytics infrastructure to identify client behavior patterns, assist in financial crime investigations, and build solutions for financial crime detection.
Perform statistical and quantitative analysis to support inclusion of risk attributes, analyze, and propose thresholds for financial crime detection controls.
Conduct research and development efforts to identify novel methods that deliver efficiency and effectiveness across analytical solutions.
Collaborate closely with stakeholders in the Global Financial Crimes, Technology and Model Risk Management departments to deliver analytics solutions from conception to deployment.
What You’ll Bring To
The Role
Advanced quantitative degree in machine learning, statistics, mathematics, data science, computer science, engineering, or other highly quantitative fields.
3+ years of hands‑on industry experience in conducting statistical analysis, developing quantitative models, and deploying production‑quality code adhering to best practices and SDLC standards.
Experience with graph databases and network analysis tools (e.g., Network
X, Neo4J).
Experience using PySpark in distributed environments like Hadoop and cloud.
In‑depth knowledge of financial crime typologies, BSA/AML regulations, and experience with financial crime–specific platforms (e.g., Actimize, Quantexa).
Strong presentation, written, and verbal communication skills, with the ability to articulate complex technical concepts to non‑technical audiences.
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
We are committed to maintaining the first‑class service and high standard of excellence that have defined Morgan Stanley for over 89 years. Our values—putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back—are not just beliefs; they guide the decisions we make every day. 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.
Senior level
Mid‑Senior
Employment type
Full‑time
Job function
Engineering and Information Technology
Morgan Stanley is an equal‑opportunity employer. We work to provide a supportive and inclusive environment where all individuals can maximize their full potential.
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