Data Scientist
RemoteToronto, Ontario, Canada
Job Summary
Lead applied AI projects from concept to impact by prototyping, validating, and deploying practical ML and GenAI solutions for malicious behavior detection, anomaly detection, and fraud analysis. Act as an internal consultant across product, engineering, and security teams to scope problems, evaluate approaches, and advise on best practices. Design robust experiments and evaluation pipelines for model reliability, then bridge research and production by translating insights into scalable APIs and workflows. Explore new techniques including LLMs, embeddings, RAG, and agentic workflows to enhance developer and security experiences while ensuring compliance with data-privacy regulations. Communicate technical tradeoffs to stakeholders and mentor peers to elevate organizational AI literacy.
Required Qualifications
- 5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research
- Strong Python skills and practical experience with data and AI libraries/platforms such as Databricks, and LLM APIs, scikit-learn
- Experience building and shipping ML or GenAI applications—from early prototype through usable internal or customer-facing workflows
- Deep familiarity with modern LLM ecosystems, including OpenAI, Anthropic/Claude, Hugging Face, and open-weight models
- Ability to select models and design effective LLM applications using prompting, context management, structured outputs, retrieval, and tool use
- Experience building agentic or multi-step AI workflows with LangGraph, LangChain, Semantic Kernel, or similar orchestration frameworks
- Strong evaluation mindset: defining useful quality metrics, building representative evaluation datasets, assessing reliability, and making data-driven tradeoffs
- Comfortable working with large, messy, structured, and unstructured data to produce features, insights, and clear visualizations
- Proficiency with Git, testing, code review, and collaborative software-development practices
- Proactive and accountable, with strong written and verbal communication skills across technical and non-technical partners
Desired Qualifications
- Computer Science or equivalent technical degree strongly preferred
- Strong MLOps experience, including MLflow or comparable tooling, experiment tracking, reproducible pipelines, model/application versioning, CI/CD, serving, and production monitoring
- Experience operating ML or GenAI systems at scale, including observability, tracing, incident response, and data or model-drift detection
- Experience with Databricks ML, AWS SageMaker, Azure ML, or similar managed ML platforms
- Familiarity with MCP, agent-tool integrations, LLM guardrails, and production safety practices
- Experience with AI-assisted development tools such as Copilot, Claude Code, or Codex
- Exposure to cybersecurity, fraud detection, anomaly detection, code analysis, or software supply-chain security
- Experience with PySpark and production data pipelines
- Experience working within a software product company or SaaS
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