About the Role
Sonatype is the software supply chain management company that invented componentized software development and pioneered the software supply chain category. As leaders in the open-source community and the DevSecOps industry, we run the world's largest repository of Java open-source components—Maven Central.
Our groundbreaking, full-spectrum platform empowers customers to rapidly create, deploy, and maintain innovative software at scale, all while aligning directly to their business needs. Trusted by more than 2,000 organizations—including 70% of the Fortune 100—and over 15 million software developers, Sonatype's tools and guidance help deliver exceptional, secure software.
From inventing modern artifact management with Nexus Repository to introducing the world's only solution that halts malicious open-source malware in its tracks, we're committed to constant innovation. We leverage AI/ML to give our clients, developers, and the industry complete confidence in the quality, automation, and security of their software.
Learn more at www.sonatype.com
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:
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Lead applied AI projects from concept to impact — prototype, validate, and help teams deploy practical ML and GenAI solutions.
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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.
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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.
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Design robust experiments and establish evaluation pipelines for model reliability, accuracy, and business impact (cross-validation, drift monitoring, ground-truth evaluation).
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Bridge research and production: translate research insights into scalable APIs, tools, or workflows that enable other teams to adopt AI effectively.
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Explore new techniques (LLMs, embeddings models, RAG, agentic workflows) to enhance developer and security experiences.
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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.
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Partner with our data governance team to ensure compliance with data-privacy regulations and ethical considerations when working with customer data.
What you bring:
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5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research.
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Computer Science or equivalent technical degree strongly preferred
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Strong Python skills and practical experience with data and AI libraries/platforms such as Databricks, and LLM APIs, scikit-learn
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Experience building and shipping ML or GenAI applications—from early prototype through usable internal or customer-facing workflows.
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Deep familiarity with modern LLM ecosystems, including OpenAI, Anthropic/Claude, Hugging Face, and open-weight models.
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Ability to select models and design effective LLM applications using prompting, context management, structured outputs, retrieval, and tool use.
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Experience building agentic or multi-step AI workflows with LangGraph, LangChain, Semantic Kernel, or similar orchestration frameworks.
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Strong evaluation mindset: defining useful quality metrics, building representative evaluation datasets, assessing reliability, and making data-driven tradeoffs.
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Comfortable working with large, messy, structured, and unstructured data to produce features, insights, and clear visualizations.
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Proficiency with Git, testing, code review, and collaborative software-development practices.
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Practical, balanced judgment: comfortable exploring emerging AI capabilities while building maintainable, secure, dependable systems.
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Proactive and accountable, with strong written and verbal communication skills across technical and non-tech
