Senior Data Science Engineer (Canada, EST, Remote)

MindBridge; Ottawa; Hybrid; CAD 179,000 - 214,000; Full time

Benefits offered by this job

Flexible hybrid/remote work

Comprehensive health benefits

Professional development

Flexible Time Off

Equity and retirement plans

Digital detox days

Job summary

MindBridge is seeking a Senior Data Science Engineer in Canada (EST, remote) to provide applied data science expertise within the Success Engineering team, configuring and tuning models, evaluating data, and translating findings into practical solutions post-launch. You will collaborate with Success Engineering, Product, Engineering, and AI/ML teams to solve complex customer needs.

The role requires 5+ years of data science experience, strong SQL/Python skills, and ability to communicate model

Qualifications

• 5+ years of applied experience in data science, analytics engineering, or a closely related technical discipline.
• Strong SQL and Python skills.
• Data literacy and independent data investigation.
• Demonstrated ability to translate technical model behavior into terms a non-technical stakeholder can act on.
• Direct experience working with enterprise customers on technical questions.
• Credible with model internals and engineering constraints to partner with Engineering/AI/ML teams.

Responsibilities

• Maintain deep working knowledge of MindBridge's core detection methodologies and how ensembles combine control points.
• Serve as the go-to technical resource within Success Engineering for model/ensemble questions.
• Maintain ongoing relationships with Product, Engineering, and AI/ML teams to stay current on model changes and limitations.
• Provide clear technical briefs when configurability gaps are identified and explain value requirements beyond administrative escalation.
• Explain model behavior to finance, audit, and compliance stakeholders to aid decision-making.
• Escalate appropriately and avoid bespoke workarounds, documenting issues and governance.

Skills

5+ years DS

SQL

Python

Explainability

Cross-functional collaboration

Customer-facing experience

Job description

Senior Data Science Engineer (Canada, EST, Remote)

MindBridge Analytics, 80 Aberdeen Street, Ottawa, Ontario, Canada

Job Description

Posted Monday, August 24, 2026 at 4:00 a.m.

MindBridge is the global leader in AI-powered financial risk intelligence. Our platform, MindBridge AI™ is enabling finance and audit professionals to build the AI-powered finance department of the future. With over 120 billion financial transactions analyzed with MindBridge’s AI, we set the standard for innovation, scalability, and customer satisfaction.

At MindBridge, we're driven by innovation and excellence, united as a team to revolutionize financial integrity. Here, your ideas matter, and your efforts make a meaningful impact. If you're passionate about using AI to drive positive change, MindBridge is the perfect fit. What distinguishes us is our unwavering commitment to our values: Innovation, Collaboration, and Integrity. These principles foster a vibrant workplace culture, where appreciation and a strong sense of community flourish.

Role Overview

We are looking for a Senior Data Science Engineer to provide applied data science expertise within our Success Engineering team, helping customers maximize the value of MindBridge’s control points and ensembles. You will configure and tune existing models, assess their application to customer data, investigate model behaviour and results, and translate complex findings into practical solutions. Working primarily post-launch, you will partner closely with Success Engineering, Product, Engineering, and AI/ML teams to solve complex customer needs within MindBridge’s existing capabilities.

What You Will Do

• Maintain deep working knowledge ofMindBridge'score detection methodologies: scoring logic, risk indicators, and how ensembles combine individual control points into a single output.
• Serve as the go-to technical resource within Success Engineering for questions about how a model or ensemble actually works.
• Maintain an active, ongoing working relationship with Product, Engineering, and AI/ML teams to stay current on model changes, known limitations, and upcoming capability shifts.
• Use that relationship to bring well-informed, technically grounded context back to Product/Engineering when a configurability gap is identified, a clear technical brief on what was requested, why it isn't currently supported, and what the customer's underlying value requirement needs, not just an administrative escalation.

Configurability boundaries & value mapping

• Develop andmaintainauthoritative understanding of how, why, and to what extentMindBridge'score models and ensembles can be configured, which parameters are flexible, which are structurally fixed, and the statistical or product reasoning behind each boundary.
• Map a customer's stated business value requirement onto the specific configuration options actually available, and explain in plain terms what is, and is not, achievable within the current product.

Post-launch feasibility & configuration advisory

• Evaluate customer requests to add,modify, or reconfigure a control point or ensemble, anddeterminewhether it isfeasiblewith existing product capability and the customer's available data.
• Define the specific data requirements: fields, quality, volume, structure — needed to support a proposed configuration.
• Recommend the configuration approach that best fits the customer's control objective within supported product capability.

Explainability & customer communication

• Translate model and ensemble behavior into language finance, audit, and compliance stakeholders can act on: what it measures, how it scores, and why a specific result occurred.
• Support customers who need to justify or defend MindBridge's outputs to their own internal or external stakeholders, a recurring requirement in audit-facing use cases.

When a control point or ensemble underperforms post-launch,determinewhether the cause is data quality, configuration, or a genuine product limitation, and recommend the correct fix.

Escalation & product boundary stewardship

• Distinguish clearly between a configuration question and a request that requires new productcapability, androute the latter through Product/Engineering governance.
• Do not build bespoke workarounds to cover product gaps; document and escalat them instead.

Enablement & knowledge capture

Convert recurring model and configuration questions into FAQs, decision guides, and training material for Success Engineers, Success Engineering Architects, and Delivery Services.

Bounded support to Delivery Services

• Act as an internal subject-matter-expert resource for Solutions Architects and Data Engineers specifically when an implementation calls for a control point or ensemble configuration that has not been built or validated before; not for routine,previously-validatedfeasibility questions, which remain owned by Delivery Services.
• This is consultative, time-boxed input at the point a novel configuration is being designed, not ownership of the Technical Requirements Blueprint or any implementation deliverable, which remains with Delivery Services throughout the technical blueprint phase.

Required Qualifications

• 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 the statistical and machine learning techniques used in anomaly and risk detection — scoring models, ensemble/combination methods, outlier detection — sufficient to reason about why a model behaves as it does, not only what it outputs; building such models from scratch is not required.
• Strong SQL, Python and data literacy; able to independently investigate whether a data set can support a proposed control point or ensemble configuration.
• Demonstrated ability to translate technical model behavior into terms a non-technical, often finance, audit, or compliance stakeholder can act on.
• Direct experience working with enterprise customers on technical questions, in a support, technical account management, implementation, or applied customer-facing data science capacity.
• Credible enough with model internals and engineering constraints to partner directly with Engineering and AI/ML teams as a peer. This role sits between the customer-facing and product-technical sides, not solely on the customer-facing side.
• Comfortable operating inside a defined product boundary: recommending within existing capability and escalating clearly rather than building around gaps.

Preferred Qualifications

• Experience in audit, internal controls, financial risk, or fraud analytics.
• Familiarity with explainability and interpretability expectations in regulated or audit-facing environments.
• Experience producing enablement content i.e. FAQs, playbooks, training materials, for internal technical teams.
• Prior experience in a dedicated post-implementation optimization function, as distinct from initial delivery.
• Background in ML engineering, applied statistics, or a related technical field with direct exposure to production model constraints; not solely academic or research modeling experience.

Requirements contingent on employment

• Fulfill requirements necessary to obtain full background check.

Pay Range

The expected base salary range for this position is to $130,000 to $155,000 and may be eligible for bonus awards. The determination of an applicant’s base salary within this range is based on the individual’s location, skills, experience and competencies, and unique qualifications.

Why You’ll Love Being Part of Our Team:

• Competitive Compensation and Equity
• Flexible Work – Hybrid or Remote
• Comprehensive health benefits and wellness programs
• Professional development opportunities
• Flexible Time Off
• Company Matched Retirement Plans
• Unplug and recharge - 4 company-wide digital detox days annually

Equal Opportunity at MindBridge

At the heart of our global success lies commitment to diversity and inclusion. We rigorously enforce an equal opportunity policy in all aspects of employment, championing merit and qualifications as our benchmarks. MindBridge is a proud equal opportunity employer, embracing applicants of all backgrounds without regard to race, nationality, religion, gender, disability, or any other factors.
At MindBridge, we are committed to providing an accessible candidate experience. If you require accommodations during the interview process or beyond, please inform us. We will work with you to provide necessary support and reasonable accommodations while maintaining confidentiality. Your comfort and participation are paramount to us.

Please be advised that we may use AI tools in the processing of your application.