Data Scientist
New
S
SonatypeSoftware Supply Chain
Canada - Remote; Toronto - RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonGitMachine LearningData scienceDatabricksscikit-learnGenerative AILangChain
Requirements
- 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 prototype through to production.
- Deep familiarity with modern LLM ecosystems including OpenAI, Anthropic/Claude, Hugging Face, and open-weight models.
- Ability to design effective LLM applications using prompting, context management, and structured outputs.
- Experience building agentic or multi-step AI workflows with LangGraph, LangChain, Semantic Kernel, or similar frameworks.
- Strong evaluation mindset: defining quality metrics, building evaluation datasets, and assessing model reliability.
- Comfortable working with large, messy, structured, and unstructured data.
- Proficiency with Git, testing, code review, and collaborative software-development practices.
Responsibilities
- 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 to scope problems and advise on AI best practices.
- Develop models for malicious behavior detection, anomaly detection, and fraud analysis using classical ML to LLMs, embeddings, and agentic workflows.
- Design robust experiments and evaluation pipelines for model reliability, accuracy, and business impact.
- Translate research insights into scalable APIs, tools, or workflows that enable other teams to adopt AI effectively.
- Communicate technical concepts and recommendations to technical and non-technical stakeholders.
- Partner with data governance to ensure compliance with data-privacy regulations and ethical standards.
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