Data Scientist

• 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
• 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

Benefits

• Competitive benefits package
• Flexible work and schedule
• Annual wellness week
• Paid volunteer time off
• Full parental/maternity (birth or adoption) leave- Strong evaluation mindset: defining useful quality metrics, building representative evaluation datasets, assessing reliability, and making data-driven tradeoffs
• Deep familiarity with modern LLM ecosystems, including OpenAI, Anthropic/Claude, Hugging Face, and open-weight models
• Experience building and shipping ML or GenAI applications—from early prototype through usable internal or customer-facing workflows
• Proficiency with Git, testing, code review, and collaborative software-development practices
• Computer Science or equivalent technical degree strongly preferred
• Proactive and accountable, with strong written and verbal communication skills across technical and non-technical partners
• Strong Python skills and practical experience with data and AI libraries/platforms such as Databricks, and LLM APIs, scikit-learn
• Comfortable working with large, messy, structured, and unstructured data to produce features, insights, and clear visualizations
• Practical, balanced judgment: comfortable exploring emerging AI capabilities while building maintainable, secure, dependable systems
• Experience building agentic or multi-step AI workflows with LangGraph, LangChain, Semantic Kernel, or similar orchestration frameworks
• 5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research
• Ability to select models and design effective LLM applications using prompting, context management, structured outputs, retrieval, and tool use
• 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