Senior Data Science Engineer, Canada

This is a fully remote position, open to applicants in Canada.

📋 Description

• Deliver applied data science expertise within the Success Engineering team.

• Configure and optimize existing models, evaluate their application to customer data, analyze model behavior and results, and convert findings into actionable solutions.

• Maintain an in-depth understanding of MindBridge's detection methodologies, scoring logic, risk indicators, and ensemble outputs.

• Act as the technical resource within Success Engineering for inquiries related to models and ensembles.

• Collaborate with Product, Engineering, and AI/ML teams on model modifications, limitations, capability changes, and configurability gaps.

• Keep an authoritative grasp of model and ensemble configurability limits.

• Align customer business value needs with available configuration options and articulate achievable outcomes.

• Assess post-launch requests to add, alter, or reconfigure control points or ensembles.

• Specify data requirements for proposed configurations, including fields, quality, volume, and structure.

• Recommend configurations that meet customer control objectives and align with supported product capabilities.

• Clarify model and ensemble behavior to finance, audit, and compliance stakeholders.

• Assist customers in justifying or defending MindBridge outputs.

• Diagnose underperformance issues stemming from data quality, configuration, or product limitations, and suggest remedies.

• Differentiate configuration inquiries from requests necessitating new product capabilities and route escalations through Product/Engineering governance.

• Transform recurring questions into FAQs, decision guides, and training materials.

• Provide bounded, consultative, time-limited subject-matter-expert support to Delivery Services for new configurations without taking ownership of implementation deliverables.

⛳️ Requirements

• Over 5 years of applied experience in data science, analytics engineering, or a closely related technical field, preferably supporting enterprise software customers post-implementation.

• Proficient in statistical and machine learning techniques utilized in anomaly and risk detection, including scoring models, ensemble/combination methods, and outlier detection.

• Strong skills in SQL, Python, and data literacy.

• Ability to independently assess whether a dataset can support a proposed control point or ensemble configuration.

• Proven capability to translate technical model behavior into actionable insights for non-technical finance, audit, or compliance stakeholders.

• Direct experience collaborating with enterprise customers on technical inquiries in a support, technical account management, implementation, or applied customer-facing data science role.

• Ability to engage directly with Engineering and AI/ML teams as a peer.

• Comfortable operating within defined product constraints and escalating product gaps rather than creating workarounds.

• Experience in audit, internal controls, financial risk, or fraud analytics.

• Familiar with explainability and interpretability expectations in regulated or audit-facing environments.

• Experience in producing FAQs, playbooks, or training materials for internal technical teams.

• Previous experience in a dedicated post-implementation optimization role.

• Background in ML engineering, applied statistics, or a related technical field with direct exposure to production model constraints.

• Meet the requirements necessary to complete a full background check.

🏝️ Benefits

• Potential eligibility for bonus awards.

• Completion of full background check requirements is necessary for employment.