ZS

Toronto
15,000 Total Employees
Year Founded: 1983

Jobs at ZS

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Recently posted jobs

23 Hours AgoSaved
Hybrid
Toronto, ON, CAN
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
Design, develop, and deploy scalable LLM-based solutions (RAG, agentic workflows, PEFT). Lead architecture, implement cloud and DevOps best practices (AWS, Azure, Databricks, Terraform, Docker, Kubernetes), build prompt/response guardrails, collaborate cross-functionally, and manage full SDLC and production deployments.
3 Days AgoSaved
Hybrid
Toronto, ON, CAN
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
The Decision Analytics Associate Consultant will develop statistical models, leverage analytics, design analyses, and communicate results to clients while mentoring associates and managing client relationships.
5 Days AgoSaved
Hybrid
Toronto, ON, CAN
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
Design and develop Large Language Model-based applications within Pharma R&D, focusing on client collaboration and technical excellence while ensuring production readiness and performance optimization.
5 Days AgoSaved
Hybrid
4 Locations
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
The Senior Marketing Analytics Lead will oversee analytics projects, translate data into insights, influence marketing strategies, and mentor junior team members.
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
As a Strategy Insights & Planning Associate Consultant, you will leverage problem solving, conduct market research, and develop client relationships while mentoring team members. You will structure analyses, communicate results, and ensure project goals are met, while supporting ZS's capabilities.
12 Days AgoSaved
Hybrid
Toronto, ON, CAN
Artificial Intelligence • Healthtech • Professional Services • Analytics • Consulting
Design, build, and maintain scalable data pipelines and storage (data lakes/warehouses). Integrate diverse sources, enforce data quality, security and compliance, optimize ETL/ELT, monitor pipelines, and mentor junior engineers while collaborating with data scientists, analysts, and stakeholders.