Manager, Applied AI/ML, Data Science & Engineering
New
Canada, Global time zones (North America, Europe, India)Full-TimeManager
SalaryCanada-based range typically $158,000–$231,000
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Job Details
- Experience
- 7+ years
- Required Skills
- Data engineeringData scienceDevOpsStakeholder managementDistributed Systems
Requirements
- 7+ years of experience in engineering leadership, applied AI/ML, data science, or data platform environments.
- Proven ability to manage and mentor senior and principal-level technical contributors.
- Strong technical foundation in modern software engineering, data systems, and AI/ML development practices.
- Experience working in environments where processes, standards, and workflows are evolving or being redefined.
- Deep curiosity about AI-driven transformation of engineering practices and delivery models.
- Strong cross-functional leadership experience across engineering, product, infrastructure, QA, and business teams.
- Excellent written communication skills, including documentation, planning, and stakeholder updates in distributed environments.
- Strong verbal communication skills, including facilitation, conflict resolution, and executive engagement.
- Experience working with globally distributed teams across North America, Europe, and India.
- Strong execution and program management skills, including prioritization, dependency tracking, and risk management.
- Generalist mindset with willingness to engage across DevOps, cloud infrastructure, data platforms, and delivery systems.
- Ability to empower strong ICs without micromanagement while maintaining alignment and accountability.
Responsibilities
- Lead, mentor, and grow a distributed team of senior and principal engineers and data scientists working on applied AI/ML systems, data platforms, and engineering infrastructure.
- Define clarity in ambiguous environments by establishing priorities, execution plans, success criteria, and decision-making frameworks.
- Build and optimize operating rhythms across global teams, including asynchronous communication practices, documentation standards, and cross-time-zone collaboration models.
- Partner with technical leads and ICs to translate strategic goals into scoped initiatives, milestones, and measurable outcomes.
- Guide the evolution of AI/ML engineering practices, including prototyping, evaluation frameworks, production readiness, governance, and quality standards.
- Drive continuous improvement in delivery processes while adapting traditional engineering models to AI-driven workflows.
- Support cross-functional collaboration across product, engineering, data science, infrastructure, QA, and business stakeholders.
- Coach team members in communication, prioritization, technical judgment, stakeholder management, and distributed execution.
- Identify and resolve risks, dependencies, and bottlenecks early to maintain execution momentum and delivery quality.
- Recruit, onboard, and develop strong generalist technical talent across AI/ML, data systems, and platform engineering domains.
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