Senior Data Science Engineer, Canada

Job not on LinkedIn

🕒 August 25

🇨🇦 Canada – Remote

💵 $130k - $155k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Mindbridge

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

Founded 2004

🤝 B2B

💼 Consulting

🏢 Enterprise

B2B • Consulting • Enterprise

Mindbridge is one of the fastest-growing business process outsourcing (BPO) companies, providing end-to-end customer experience and back-office services that combine automation, analytics and human teams. They offer omnichannel CX management (chat, voice, email), 24/7 multilingual support, B2B sales, content moderation, creative design and animation, financial & accounting services including KYC and screening, document digitization, market surveys and analytics. Mindbridge serves tech and disruptive companies across fintech, telecom, ride-hailing, food delivery, social media and startups, operating in 25+ languages across EU, MENA, APAC and North America and handling large daily volumes (60k+ emails, 25k+ chats, 110k+ calls, 20k+ surveys).

📋 Description

• 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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