Lead Data Scientist developing AML transaction - monitoring models for RBC, a major Canadian bank. Applying machine learning, rules, and analytics to identify financial crime risk.
About the role
- Senior data science engineer optimizing MindBridge’s audit-risk models for enterprise customers. Advising on configurations, explainability, diagnostics, and product-boundary escalations.
Responsibilities
- Provide applied data science expertise within the Success Engineering team
- Configure and tune existing models, assess their application to customer data, investigate model behavior and results, and translate findings into practical solutions
- Maintain deep working knowledge of MindBridge's detection methodologies, scoring logic, risk indicators, and ensemble outputs
- Serve as the technical resource within Success Engineering for model and ensemble questions
- Partner with Product, Engineering, and AI/ML teams on model changes, limitations, capability shifts, and configurability gaps
- Maintain authoritative understanding of model and ensemble configurability boundaries
- Map customer business value requirements to available configuration options and explain achievable outcomes
- Evaluate post-launch requests to add, modify, or reconfigure control points or ensembles
- Define data requirements for proposed configurations, including fields, quality, volume, and structure
- Recommend configurations aligned with customer control objectives and supported product capability
- Explain model and ensemble behavior to finance, audit, and compliance stakeholders
- Support customers in justifying or defending MindBridge outputs
- Diagnose whether underperformance results from data quality, configuration, or product limitations, and recommend fixes
- Distinguish configuration questions from requests requiring new product capability and route escalations through Product/Engineering governance
- Convert recurring questions into FAQs, decision guides, and training material
- Provide bounded, consultative, time-boxed subject-matter-expert support to Delivery Services for novel configurations without owning implementation deliverables
Requirements
- 5+ years of applied experience in data science, analytics engineering, or a closely related technical discipline, ideally supporting enterprise software customers after implementation
- Working knowledge of statistical and machine learning techniques used in anomaly and risk detection, including scoring models, ensemble/combination methods, and outlier detection
- Strong SQL, Python, and data literacy
- Ability to independently investigate whether a data set can support a proposed control point or ensemble configuration
- Demonstrated ability to translate technical model behavior into actionable terms for non-technical finance, audit, or compliance stakeholders
- Direct experience working with enterprise customers on technical questions in a support, technical account management, implementation, or applied customer-facing data science capacity
- Ability to partner directly with Engineering and AI/ML teams as a peer
- Comfort operating within defined product boundaries and escalating product gaps rather than building workarounds
- Experience in audit, internal controls, financial risk, or fraud analytics
- Familiarity with explainability and interpretability expectations in regulated or audit-facing environments
- Experience producing FAQs, playbooks, or training materials for internal technical teams
- Prior experience in a dedicated post-implementation optimization function
- Background in ML engineering, applied statistics, or a related technical field with direct exposure to production model constraints
- Fulfill requirements necessary to obtain full background check
Benefits
- May be eligible for bonus awards
- Full background check requirements must be fulfilled for employment
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