The Data Science group serves under the Black Duck Data Engineering organization as a center of excellence in data analysis, statistical interpretation, machine learning engineering, attribution analysis and operational metric review.

Our core purview is the application of historical data to drive future decision making, and to develop and maintain machine learning and analytical tools to improve service delivery and advise operational processes.

We also serve as mediators of meaning across Black Duck, and as custodians of a shared data ecosystem.

This ecosystem serves to empower data consumers to explore client-operational and service-ops data in more intuitive ways, and supports their ability to share and collaborate on best practices for using this data in truthful, responsible, ethical and efficient ways.

Our Values

• Trust: Our work has no value if it is not trusted by our colleagues/customers, or if our work is not respectable. We will always rather be late than incorrect.
• Collaboration: We have a “very specific set of skills”, however we should humbly respect the subject matter experts we work with in their fields of expertise; we’re experts shuffling bits around faster/better/smarter, but they do the real work.
• Results: For our work to have value, it must speed up, augment, or replace, a current decision making process, or to demonstrate a new product/operational opportunity. While the “value” of a result may not be known for a long time, we are still outcome-driven.
• Curiosity: Exploration and Experimentation is at the heart of what we do, and we empower each other with the freedom to explore, and possibly get lost in, longer term research projects than other groups. However, even those dead ends have value when they’re written up. (It’s not science unless you write it down)
• Fun: We’re in this business because we enjoy the strange and often incongruous world of data, and what that data can tell us about ourselves. Revel in the comedy of your mistakes and discoveries, and share them with abandon.

About the Role

As a Data Engineer/Scientist, you will be a custodian of our cross-functional data regime, and a driver of innovative uses of Data across our cybersecurity platforms, ranging from predictive analytics and customer behavioural analysis, through to training customised machine learning

models on continuously evolving feedback streams from data and decisions that really matter to our thousands of customers that rely on our security assessments for safety, stability, and often, sleep.

The ideal candidate is a self-starter who is enthusiastic about exploring new technologies. They also enjoy helping their peers and their organization remain at the forefront of our industry by identifying and helping implement the latest industry best-practices that boost our organization’s productivity and team members’ experience.

The role is primarily based around our Belfast R&D Site, but UK/EMEA remote or hybrid applicants will be considered. At least Quarterly travel to the Belfast R&D Site is expected, and additional travel / conference opportunities may be available depending on your impact and collaboration.

Key Responsibilities:

• Developing and maintaining analytical data pipelines from a range of sources, internal and external
• Participate in system design discussions and contribute to architectural decisions.
• Evaluating new analytical / technological opportunities for leveraging those data for security/business impact
• Leading projects from research through to production deployment and operational handover to appropriate teams
• Partnering with R&D and Engineering teams to develop and share best practices for data tooling, from pipelines and dashboards to ML and LLM integration

Key Qualifications:

• 5+ years of experience working in Data Science, AI/Data Engineering, Data Operations, DevOps, Business Analytics, or a related field
• BSc Or MSc in Computer Science, Data Science, Artificial Intelligence, Math, Physics, Engineering or related field/degree
• Experience in a relevant analytical programming language the point where you can build / deliver a project/module from scratch that can be used by others (Python is our main daily-driver, expert-level experience in Julia or Rlang could be accepted
•  Experience in Jupyter Notebook / equivalents
• Experience in Airflow, DBT, Databricks, or equivalent stacks
• Experience in the PyData / Spark or equivalent analytical stacks
• Familiarity with Cybersecurity Governance, Application Security Testing, Quality Assurance or similar
• Experience in data modelling and working with RDBMS (PostgreSQL, Oracle or MySQL) and knowledge of NoSQL databases (e.g. MongoDB)
• Experience with Machine Learning and AI systems
• Hands-on experience with AI-assisted development tools (e.g., GitHub Copilot, Claude Code, Cursor, or similar)
• Independent project operation and cross-functional collaboration
• Strong or Developing communication skills (in-person and remote)

Nice to have:

• Familiarity with Data Mesh/Data Product concepts
• Experience in operating in Linux Command line environments
• Experience in Langchain or equivalent Agentic development stack
• Experience in training custom Machine Learning models, including familiarity with evaluation criteria and metric design
• Experience in integrating AI capabilities into software systems, including prompt engineering, API integration, and leveraging LLM-based services for automation and productivity
• Experience in Enterprise Data Visualisation such as Power BI, Tableau, Grafana, DataBricks, Snowflake etc.
• Experience deploying ML/AI models in production environments/workloads
• Experience in developing/working within large enterprise applications using microservices architecture, and container orchestration technologies, running on Kubernetes and/or cloud technologies (AWS, Azure or GCP)
• Experience in software architecture, systems design, interaction design (to the point where you can have constructive conversations with security / architecture leaders)