Lead, Customer Strategy Analytics & Applied AI
Remote-first work environment with flexibility to work from anywhere in Canada.Full-TimeLead
Salary$190,000 – $210,000 (CAD or equivalent)
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Job Details
- Experience
- 5–7+ years
- Required Skills
- PythonSQLMachine LearningStrategySaaSLLM
Requirements
- 5–7+ years of experience in analytics, strategy, operations, consulting, or a hybrid technical-business role.
- Proven track record of shipping data products, machine learning models, or AI-powered systems into production environments.
- Strong quantitative and analytical skills, with familiarity in SQL and/or Python considered a plus.
- Hands-on experience or strong familiarity with applied AI tools and agentic systems (e.g., modern LLM-based tooling).
- Ability to quickly understand Customer Success or Support ecosystems and translate insights into operational impact.
- Self-directed operator capable of owning end-to-end workstreams without heavy scaffolding.
- Strong product mindset with a bias toward building scalable systems over one-off analyses.
- Comfort working in ambiguity, defining priorities, and solving novel problems without predefined playbooks.
- Experience in SaaS, marketplaces, or high-growth tech environments is preferred.
Responsibilities
- Design and deliver data products, predictive models, and AI-powered systems such as churn risk scoring, next-best-action frameworks, and automated retention playbooks.
- Lead strategic analytics efforts across the customer lifecycle, partnering directly with senior leadership to define CS investment priorities and operating models.
- Build scalable analytics infrastructure and workflows that enable teams to act on insights without manual analysis dependency.
- Guide and prioritize work within your analytics pod, shaping execution done by more junior team members and ensuring alignment with strategic goals.
- Develop AI-enabled systems that improve CS and Support efficiency, including engagement scoring, customer health monitoring, and automation pipelines.
- Partner cross-functionally with Product, Sales, RevOps, Enablement, and Analytics Engineering teams to ensure data quality, modeling integrity, and business alignment.
- Drive adoption of AI tools and methodologies across the broader Business Operations function to accelerate analytical output and decision-making.
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