Black Duck Software, Inc. helps organizations build secure, high-quality software, minimizing risks while maximizing speed and productivity. Black Duck, a recognized pioneer in application security, provides SAST, SCA, and DAST solutions that enable teams to quickly find and fix vulnerabilities and defects in proprietary code, open source components, and application behavior. With a combination of industry-leading tools, services, and expertise, only Black Duck helps organizations maximize security and quality in DevSecOps and throughout the software development life cycle.
The Data Science group sits within Black Duck's Data Engineering organisation, operating as a centre of excellence in statistical analysis, machine learning engineering, and applied AI. We work at the intersection of cybersecurity and data intelligence; building models, evaluation frameworks, and AI-powered capabilities that directly shape how tens of thousands of developers and security teams understand and respond to risk.
Our work spans predictive analytics, behavioural modelling, LLM integration, and the development of internal AI platforms. We're opinionated about quality, curious by default, and outcome-driven in everything we ship.
Trust: We'd rather be late than wrong. Our work is only as valuable as its credibility.
Collaboration: We have deep technical expertise, but we work shoulder-to-shoulder with the subject matter experts who define what "correct" actually means in our domain.
Results: We're outcome-driven. Research has value, but it has more value when it's written up, shipped, or handed off.
Curiosity: Exploration is core to what we do — including the dead ends. (It's not science unless you write it down.)
Fun: We work in a genuinely strange corner of the data world. Revel in it.
As a Senior Data Scientist, you will own the design, development, and production deployment of machine learning and AI systems that drive measurable outcomes across Black Duck's cybersecurity platforms. This is primarily a technical individual contributor role — we're looking for someone who can take a problem from framing through to a shipped, evaluated, production system, and who brings genuine depth in ML/AI engineering rather than general data work.
Data infrastructure and pipeline ownership sits with our dedicated Data Engineering function. Your focus is on what we do with that data: building models, evaluating them rigorously, integrating AI capabilities into products and internal tooling, and helping shape how the team approaches applied AI at scale.
The role is primarily based at our Belfast R&D site. UK/EMEA remote or hybrid applicants will be considered, with at least quarterly travel to Belfast expected. Additional conference and collaboration opportunities are available commensurate with your impact.
Design, develop, and maintain machine learning and AI systems, from prototype through to production, with clear evaluation criteria and operational handover
Lead the integration of LLM and agentic AI capabilities into Black Duck products and internal platforms (including prompt engineering, retrieval-augmented generation, and tool/agent orchestration)
Design and own LLM evaluation frameworks: defining task-specific metrics, building offline and online eval pipelines, running structured comparisons across models and configurations, and producing clear recommendations with supporting evidence
Conduct cost-benefit analysis on AI/ML system decisions: model / architecture / methodology selection, operational costs, build-vs-buy trade-offs, and quantifying the value of AI/ML interventions against baseline approaches; communicating findings clearly to technical and non-technical stakeholders
Collaborate with R&D and Product Engineering to embed AI capabilities into existing workflows and surfaces
Contribute to the team's shared practices around model governance, reproducibility, and responsible AI use
Mentor team members, peers and the wider organisation on evolving and emerging ML/AI engineering practices and help continuously maintain the organisation's technical standards
5+ years of hands-on experience in data science, machine learning engineering, or applied AI — with demonstrable delivery of production ML/AI systems, not just research or analysis
Strong Software engineering proficiency; you can design and deliver a project or module from scratch that others can build on
Experience deploying ML/AI models in production on cloud infrastructure (AWS, Azure, or GCP) and/or Kubernetes workloads
Practical experience with ML/AI development stacks: PyTorch, scikit-learn, HuggingFace or equivalent; experiment tracking (MLflow, W&B or similar); and model evaluation tooling
Experience designing and executing LLM evaluations: building eval datasets, defining metrics, running model comparisons, and translating results into actionable decisions
Experience conducting cost-benefit or trade-off analysis on AI systems; weighing inference cost, latency, accuracy, and operational complexity against business or product value
Familiarity with agentic AI patterns: tool use, multi-step reasoning, agent orchestration (LangChain, LangGraph, or equivalent)
Experience working with LLMs via API integration, prompt engineering, and RAG pipelines
Experience with Jupyter and standard scientific Python (pandas, numpy, scipy)
Ability to operate independently on multi-month projects, manage your own priorities, and communicate clearly across engineering and non-engineering stakeholders
Hands-on experience with AI-assisted development tools (GitHub Copilot, Claude Code, Cursor, or similar)
Degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, Physics, or a related field (or demonstrated equivalent through portfolio and track record)
Familiarity with cybersecurity concepts, application security testing, or software supply chain risk
Experience with data/feature stores, model registries, or MLOps tooling (Airflow, DBT, Databricks, or equivalent)
Familiarity with Data Mesh or Data Product concepts
Experience with enterprise data visualisation (Power BI, Grafana, Snowflake, Databricks dashboards)
Comfort in Linux/CLI environments and experience contributing to shared codebases (Git, code review, CI/CD)
Track record of written or public communication: internal documentation, papers, blog posts, or conference contributions
Black Duck is an equal opportunity employer. We consider all applicants for employment without regard to race, color, national origin, religion, sex, gender identity or expression, age, disability, sexual orientation, veteran or military service status, or any other characteristic protected by applicable law. Black Duck complies with all applicable laws prohibiting employment discrimination in every jurisdiction where it operates and provides reasonable accommodations to individuals with disabilities in accordance with applicable law.
