Data Scientist

Team: Engineering - Data Services

Location: Toronto - Remote, Canada - Remote

Commitment: Full-Time

Workplace Type: remote

The Role

We're looking for a Data Scientist to join our growing AI & Data Science team. You'll operate as an internal AI consultant and technical lead, helping multiple teams across Sonatype apply machine learning and generative AI to real-world problems — from malicious-behavior and anomaly detection in our security data, to developer- and analyst-facing GenAI experiences.

You'll explore complex datasets, design experiments, build and validate models, and collaborate closely with product, engineering, and security experts to turn research ideas into practical, scalable solutions. We have a mature data engineering team, so you can focus on doing what you do best — building and shipping models.

This role is ideal for someone who thrives on autonomy, loves translating ambiguous ideas into working systems, and enjoys working across boundaries rather than staying in a single product lane.

What you'll do:

Lead applied AI projects from concept to impact — prototype, validate, and help teams deploy practical ML and GenAI solutions.

Act as an internal consultant across product, engineering, security, and research teams: scope problems, evaluate approaches, and advise on ML/AI best practices and productive use of generative technologies.

Lead the research, development, and deployment of models for use cases such as malicious behavior detection, anomaly detection, and fraud analysis — using techniques ranging from classical ML to LLMs, embeddings, retrieval-augmented generation, and agentic workflows.

Design robust experiments and establish evaluation pipelines for model reliability, accuracy, and business impact (cross-validation, drift monitoring, ground-truth evaluation).

Bridge research and production: translate research insights into scalable APIs, tools, or workflows that enable other teams to adopt AI effectively.

Explore new techniques (LLMs, embeddings models, RAG, agentic workflows) to enhance developer and security experiences.

Communicate technical concepts, tradeoffs, and recommendations clearly to both technical and non-technical stakeholders through presentations, documentation, and collaboration; mentor peers and help elevate the organization's AI literacy and capabilities.

Partner with our data governance team to ensure compliance with data-privacy regulations and ethical considerations when working with customer data.

What you bring:

5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research.

Computer Science or equivalent technical degree strongly preferred

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.

Practical, balanced judgment: comfortable exploring emerging AI capabilities while building maintainable, secure, dependable systems.

Proactive and accountable, with strong written and verbal communication skills across technical and non-technical partners.

It'd be great if you had:

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.

Things we're proud of:

2026 Gartner® Magic Quadrant™ Leader for Software Supply Chain Security
2026 Celebrating 15 Years of Sonatype Research Labs – Industry-leading software supply chain and open source security research
2026 Founding Member of the Linux Foundation Initiative for Open Source Sustainability
2026 State of the Software Supply Chain® Report – Continuing industry leadership in software supply chain security and AI security research
2025 Visionary in Gartner® Magic Quadrant™ for Application Security Testing!
2025 AI Compliance Solution of the Year - AI Breakthrough Awards
2025 DEVIES Award to our SBOM Manager for a new product for its innovation and impact in developer technology
2024 Industry Leader in Forrester-Wave for Software Composition Analysis (2024 Q4 report)
Constellation AST Shortlist: Sonatype has been listed on the Constellation ShortList™ for Application Security Testing for 2024
Data Breakthrough Awards: Sonatype was announced as a 2024 winner in the "Open Source Data Solution of the Year."
SD Times: Best in Show Security
Fast Company Best Workplaces for Innovators 2024
The Herd Top 100 Private Software Companies 2024
Diversity & Inclusion Working Groups
Parental Leave Policy
Paid Volunteer Time Off (VTO)