Job Opportunities API

Hi there! Thanks for stopping by 👋 Are you actively looking for a new opportunity? Or just checking the market? Well… you might just be in the right place! We’re looking for a Manager, Data Science & Machine Learning to join our Data team in Canada. The Manager, Data Science & Machine Learning is a hands-on leader, responsible for guiding a high-performing team of data scientists to deliver impactful, production-ready solutions across the organization. This role is responsible for driving Data Science & Machine Learning model delivery from experimentation through production, owning the Data Science Enablement roadmap planning while contributing to the Data Office’s org roadmaps, and building the team capabilities needed to scale the practice. What you’ll be doing: Data Science Management & Enablement - Lead, oversee and own, as needed, the full lifecycle of Data Science & Machine Learning models from experimentation to production deployment. - Own the day-to-day management of the team by ensuring the right work is being prioritized, the team is unblocked, and delivery standards are consistently met. - Define, document, and champion data science best practices: covering modeling standards, code quality, experimentation frameworks, and documentation. - Serve as a subject matter authority and internal resource for other data science teams: advising on methodology, reviewing approaches, and helping teams solve complex or ambiguous problems. - Collaborate with Data Science leads in other parts of the business to align on standards, share learnings, and create a cohesive data science community of practice. Additionally, surfacing opportunities for collaboration, flagging where work is being duplicated, and brokering knowledge transfer across the organization. - Collaborate with the MLOps team on the production release and ongoing maintenance of their models. Team Leadership & People Development - Set clear expectations, and individual performance goals for all direct reports. - Conduct regular 1:1s, provide timely and actionable feedback, and lead performance calibrations. - Identify growth opportunities, sponsor stretch assignments, and build individualized development plans. - Foster a collaborative team culture where experimentation and learning from failure are encouraged. This includes new AI/ML features or other experimental approaches. Stakeholder Management & Communication - Participate in project planning and technical brainstorming sessions with business stakeholders and other Data Office leads as an expert to help design and translate business problems into technical briefs and communicating results in non-technical terms. - Proactively manage expectations, surface risks early, and influence across cross-functional teams. - Represent the team's work in leadership forums, steering committees, and quarterly business reviews. AND A LITTLE BIT OF.... - Contributing as part of the wider team to achieve organizational objectives even if this means doing things that aren’t strictly within the scope of your role. What you need to bring: - 3+ years of hands-on data science experience, with direct personal experience deploying models to production (not just experimentation or prototyping). - Demonstrated experience with ML engineering practices that include model serving, monitoring, drift detection, retraining pipelines, and/or feature stores. You don't need to be an engineer, but you need to manage them credibly. - Familiarity with modern MLOps tooling (e.g. MLflow, Vertex AI, Databricks). - 4+ years of experience with directly managing a team of data scientists, including hiring, performance management, and career development. - Proficiency in Python; comfortable reading and reviewing code, models, and pipeline logic. - Strong understanding of supervised/unsupervised ML, model evaluation, and common failure modes in production. - MLOps fluency to co