Senior Data Scientist

Provide technical ownership for applied AI and data science initiatives across Sonatype, helping define approaches, architecture, priorities, and standards.

Lead complex AI projects from concept to production—translating ambiguous business and technical problems into measurable experiments and scalable solutions.

Act as a senior internal AI consultant to product, engineering, security, and research teams, helping identify high-value opportunities and advising teams on ML and GenAI approaches.

Lead the research, development, and deployment of models for malicious behavior detection, anomaly detection, fraud analysis, and other security and product use cases.

Design and guide advanced GenAI solutions using LLMs, embeddings, retrieval-augmented generation, structured outputs, tool use, and agentic or multi-step workflows.

Establish strong experimentation and evaluation practices, including representative evaluation datasets, quality metrics, cross-validation, ground-truth evaluation, drift monitoring, and business-impact measurement.

Set technical direction for reliable and maintainable AI systems, balancing experimentation with production quality, security, scalability, and responsible AI practices.

Bridge research and production by designing scalable APIs, services, tools, and workflows that allow AI capabilities to be adopted across products and internal teams.

Evaluate emerging AI technologies and frameworks and make recommendations on when and how Sonatype should adopt them.

Partner closely with engineering and MLOps teams on deployment, observability, model lifecycle management, performance, and reliability.

Mentor data scientists and other technical contributors, review technical approaches, and help elevate AI engineering and data science practices across the organization.

Communicate AI strategy, technical tradeoffs, experimental findings, and recommendations clearly to technical and non-technical stakeholders.

Partner with data governance, security, and legal stakeholders to ensure appropriate privacy, security, ethical, and responsible-AI practices.

7+ 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.

Deep MLOps experience, including MLflow or comparable tooling, experiment tracking, reproducible pipelines, model/application versioning, CI/CD, serving, and production monitoring.

Experience designing or operating ML and GenAI platforms or 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.

Experience defining AI architecture, technical standards, or reusable patterns adopted across multiple teams.

Familiarity with MCP, agent-tool integrations, LLM guardrails, AI security, and production safety practices.

Experience with AI-assisted development tools such as Copilot, Claude Code, or Codex.

Experience applying ML or AI to cybersecurity, fraud detection, anomaly detection, code analysis, threat intelligence, or software supply-chain security.

Experience with PySpark and large-scale production data pipelines.

Experience working within a software product company and partnering closely with product and engineering leadership.

Experience mentoring technical contributors or acting as a technical lead across multiple AI or data science initiatives.