Fraud Data Scientist

EnStream is a leader in secure digital identity and mobile data intelligence, working to advance the future of digital trust in Canada. We build innovative data-driven models that enhance the integrity, reliability, and safety of digital identity ecosystems. Our latest initiative leverages  advanced data science ,  machine learning , and  deep learning  to further  grow and sustain digital trust  across Canada.

Our mission is to  empower frictionless trust in every interaction.  EnStream is dedicated to increasing trust and convenience for Canadians using real-life, verified identities and network data held by trusted telco networks. At EnStream, every team member plays a critical role in shaping our strategy and delivering meaningful impact across industries.le.

About the Role
We’re accelerating two high-priority fraud detection and prevention initiatives and need ahands-on Fraud Data Scientist. This role sits at the intersection of investigative analysis, applied data science, and cross-functional communication — turning raw fraud signals into actionable intelligence for internal teams, partners, and the broader digital trust roadmap. You will be running on hypercare support, creating fraud intelligence reporting, and building the analytical foundation for ongoing fraud protection R&D.

What You’ll Do

Serve as front-line analytical support during hypercare for two new fraud protection initiatives, responding directly to client and external partner requests, such as detailed fraud case analysis

Triage, investigate, and resolve incoming fraud-related queries within defined SLAs during the critical post-launch period

Analyze fraud patterns across categories and typologies to identify emerging trends, gaps in coverage, and opportunities for improvements of fraud detection capabilities

Translate analytical findings into concrete recommendations that inform R&D priorities

Generate and disseminate fraud intelligence reports to external data-sharing partners on a recurring and ad hoc basis while ensuring that reports are accurate, timely, and actionable for external partners with varying levels of technical sophistication.

Build and maintain fraud data products (datasets, dashboards, tools) that support ongoing external partner operations and future roadmap of digital trust products

Partner cross-functionally with engineering, product, and partner-facing teams to ensure fraud data products are accurate, scalable, and production-ready

What You Bring
Must-Have Skills & Experience

Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, or a related quantitative field (or equivalent practical experience)

Demonstrated experience in fraud analytics, risk analytics, or a related investigative/adversarial data domain.

Strong SQL skills and proficiency in a data science programming language (Python)

Experience analyzing large, messy, real-world datasets to identify patterns and anomalies

Ability to communicate technical findings clearly to both technical and non-technical audiences, including external partners

Comfort operating in an ambiguous, fast-moving environment with evolving requirements

Preferred Qualification

Experience with entity resolution, identity graph analysis

Prior experience supporting a product launch or hypercare period

Experience building or maintaining data pipelines, feature stores, or ML-based detection models

Background in financial crimes, trust & safety, cybersecurity, or adjacent risk domains

Why Join Us?

Contribute to a  national-scale initiative  defining the future of  digital trust  in Canada

Work on cutting-edge  fraud detection  applications using real-world identity data

Collaborate with a highly skilled, cross-functional team

Ready to Help Build a Safer Canada?
If you’re a systems thinker, trusted advisor, technical storyteller, and mission-driven leader, we’d love to talk.

This role requires a minimum of four (4) days per week working onsite at EnStream’s head office in Toronto; this requirement may be changed at management’s discretion.

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