Senior Data Science Engineer (Canada, EST, Remote)

Mindbridge Analytics

2d•Remote

About The Position

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.

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 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.

Nice To Haves

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.

Responsibilities

Maintain deep working knowledge of MindBridge's core 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.
Develop and maintain authoritative understanding of how, why, and to what extent MindBridge's core 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.
Evaluate customer requests to add, modify, or reconfigure a control point or ensemble, and determine whether it is feasible with 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.
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, determine whether the cause is data quality, configuration, or a genuine product limitation, and recommend the correct fix.
Distinguish clearly between a configuration question and a request that requires new product capability, and route the latter through Product/Engineering governance.
Do not build bespoke workarounds to cover product gaps; document and escalate them instead.
Convert recurring model and configuration questions into FAQs, decision guides, and training material for Success Engineers, Success Engineering Architects, and 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-validated feasibility 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.