Senior Manager​/Manager, Data Science

Your Role in Supporting Our Members

We are building an Applied Machine Learning (ML) & Decision AI Pod and are seeking a Senior Manager or Manager, Data Science to lead the development and delivery of classic machine learning and decision intelligence solutions - including forecasting, predictive models, experimentation, and production scoring, statistical and mathematical analysis.

This role is a hands‑on technical leader and people manager who will work closely with Data Scientists, ML Engineers, Analytics partners, and Cloud Engineers to take models from problem framing → Proof of Concept → production. You may also partner with vendors to accelerate delivery, ensure solutions meet internal standards, and integrate vendor‑built capabilities into our platforms.

This is a Full‑time, Permanent role and will report directly to the Director, AI Centre of Excellence. Our head office is based in Vancouver, but we are open to candidates in BC and Ontario. While this position is for an existing vacancy and provides a hybrid work arrangement, you will be expected to be on‑site for events and business demands when needed.

• Lead end‑to‑end delivery of classic ML solutions, including forecasting, predictive modeling, and scoring used to drive business decisions and automation
• Own key components of the ML lifecycle: problem framing, feature engineering, model development, evaluation, validation, deployment, and ongoing performance monitoring
• Drive experimentation and iteration using measurable outcomes (lift, accuracy, calibration, stability, fairness, and operational KPIs)
• Translate business needs into analytical approaches and decision logic that can be scaled across teams

• Lead the design and implementation of MLOps architecture, collaborating with Integration, CI/CD, platform teams, and vendors to operationalize models from PoC to production
• Define standard patterns for:
• Model packaging, versioning, approvals, and release management
• CI/CD for ML (build/test/deploy), automated validation, and gated promotion
• Monitoring for drift, data quality, performance decay, and retraining triggers
• Ensure solutions are designed for reliability, security, cost efficiency, and auditability in Azure and Databricks ecosystems
• Establish reusable templates and reference pipelines for batch and (where needed) real‑time scoring

• Partner with external vendors to deliver forecasting or ML capabilities, ensuring alignment with internal architecture, security, and engineering standards
• Validate vendor deliverables (methodology, metrics, documentation, model artifacts), and ensure successful integration into Azure ML / Databricks
• Drive knowledge transfer so internal teams can maintain and evolve solutions after delivery

• Lead and develop a pod working across Data Scientists, ML Engineers, Analytics, and Cloud Engineers
• Set clear priorities, delivery rhythms (agile ceremonies), technical direction, and quality standards
• Mentor team members in applied ML best practices and production readiness
• Collaborate with the Intelligent RPA Pod and GenAI Pod to integrate predictive scoring and decision intelligence into broader automation workflows

• Partner with managers, senior managers, and business stakeholders to prioritize the Applied ML roadmap
• Communicate trade‑offs clearly (speed vs. robustness, cost vs. accuracy, build vs. buy, batch vs. real‑time)
• Present recommendations with strong narrative and evidence, suitable for both technical and non‑technical audiences

• 8–12 years of experience in Data Science, Applied ML, Analytics Engineering, ML Engineering, or related fields
• Bachelor’s or Master’s degree (PhD preferred) in Computer Science, Engineering, Mathematics or a related field
• 2+ years of people leadership (or strong team lead experience with hiring, coaching, and delivery ownership)
• Proven experience delivering classic ML in production environments, including…