Role Description We are seeking a Data Scientist to design, build, and deliver production ready data science and machine learning solutions for enterprise and regulated clients. This is a hands-on role covering the full lifecycle - from data exploration and feature engineering through to model deployment, evaluation, and iteration. The role suits someone focused on real world delivery and measurable outcomes, rather than research in isolation. Key Responsibilities - Design, build, and deploy machine learning solutions that solve real business problems, moving from prototype to production. - Apply traditional ML (e.g., regression/classification/clustering) and deep learning techniques where appropriate, selecting models based on evidence and constraints. - Demonstrate strong ML fundamentals, including the mathematics behind models (probability, statistics, optimisation, linear algebra), and explain trade-offs clearly. - Develop and deploy ML and data science solutions from proof of concept to production. - Perform data exploration, feature engineering, and model development on large datasets. - Track experiments, metrics, and model versions (e.g. MLflow). - Collaborate with data engineers and AI engineers to integrate models into platforms. - Continuously improve models based on performance, feedback, and data drift. Qualifications - 5+ years in applied machine learning and deep learning roles. - Strong grounding in core ML concepts and their mathematical basis: - Probability & statistics, hypothesis testing, bias/variance, regularisation. - Optimisation (e.g., gradient-based methods), loss functions, evaluation metrics. - Linear algebra fundamentals used in ML (vectors/matrices, decompositions at a practical level). - Solid practical experience with traditional ML modelling (feature engineering, model selection, validation, and error analysis). - Demonstrable exposure to deep learning (architectures, training dynamics, evaluation), beyond “surface-level” familiarity. - Proven ability to build good quality software, not just models—clean code, testing, debugging, and maintainable design. - Strong programming skills (typically Python; additional languages a plus) and experience integrating ML into production systems. - A clear problem-solving mindset: structured thought process, ability to reason through ambiguous requirements, and iterate effectively. - Hands-on experience delivering ML solutions end-to-end, including prototyping, validation, and production/operations. - Experience with Databricks and Spark. - Hands-on use of MLflow or similar model lifecycle and MLOps frameworks. - Experience with deep learning frameworks (e.g. PyTorch). - Practical experience with GenAI / LLMs. - Exposure to AWS Bedrock & AWS SageMaker. - Strong SQL and data analysis skills. Nice to Have - Experience in regulated or security conscious environments. - Familiarity with model governance, monitoring, and managing model performance over time. - Exposure to production model deployment patterns. - Familiarity with Responsible AI and model governance. - Client facing or consulting experience. Benefits - Share in our success with our Quarterly Performance-Related Profit Share Scheme, where employees collectively benefit from a share of our company's profits. - Strong Career Progression & mentorship coaching through our Strength in Balance & Leadership schemes with a dedicated quarterly Pathways Career Development programme. - Flexible/remote working, Version 1 is tremendously understanding of life events and people’s individual circumstances and offer flexibility to help achieve a healthy work life balance. - Financial Wellbeing initiatives including; Pension, Private Healthcare Cover, Life Assurance, Financial advice and an Employee Discount scheme. - Employee Wellbeing schemes including Gym Discounts, Bike to Work, Fitness classes, Mindfulness Workshops, Employee Assistance Programme and much more. - Generous holiday allowance, enhanced maternity/paternity leave, marriage/civil partnership leave and special leave policies. - Educational assistance, incentivised certifications, and accreditations, including AWS, Microsoft, Oracle, and Red Hat. - Reward schemes including Version 1’s Annual Excellence Awards & ‘Call-Out’ platform. - Environment, Social and Community First initiatives allow you to get involved in local fundraising and development opportunities as part of fostering our diversity, inclusion and belonging schemes. - And many more exciting benefits… drop us a note to find out more. Company Description Version 1 has celebrated 30 years in business and continues to be trusted by global brands to deliver technology and transformation solutions that drive customer success. Our deep expertise enables our customers to navigate the rapidly evolving technology landscape. We foster strong partnerships with global technology leaders including Microsoft, AWS, Oracle, Red Hat, OutSystems, Snowflake, ensuring that our customers are provided with the highest quality solutions and services. - UK & Ireland's premier AWS, Microsoft & Oracle partner. - 3300+ strong, €350/£300m revenue business. - 10+ years as a Great Place to Work in Ireland & UK. - Best Workplace for Women in the UK & Ireland by GPTW. - Best Workplace for Wellbeing in the UK by GPTW.

• Work with colleagues from our Data & AI department and collaborate closely with product managers, engineers, and commercial stakeholders. • Design, build, and operate machine learning systems for retail media use cases such as sponsored product ranking, audience segmentation, campaign optimization, attribution, and performance measurement. • Translate business and product requirements into scalable ML solutions, balancing model quality, latency, reliability, scalability, and maintainability. • Develop robust ML pipelines for feature engineering, training, evaluation, deployment, monitoring, and continuous model improvement. • Bring models into production using our cloud-based stack and ensure they are reliable, observable, and maintainable over time. • Communicate technical decisions, assumptions, limitations, and uncertainty clearly to product, engineering, and business stakeholders. • Contribute to ML engineering standards, best practices, and knowledge sharing within the team.

• Represent Informatics as the subject matter expert in cross-functional initiatives, collaborating closely with scientists, data teams and technical stakeholders. • Identify and deliver solutions where computational and AI-driven approaches can improve efficiency, decision making, and knowledge utilization across complex workflows. • Design, develop and deploy production-ready AI-enabled applications that integrate structured and unstructured data for scalable use. • Build and implement retrieval-augmented systems (RAG), semantic search frameworks, and embedding-based data workflows to enable intelligent information retrieval. • Develop conversational AI interfaces and agentic architectures that automate knowledge-intensive tasks and support user interaction with complex datasets. • Research, design and develop machine learning algorithms, tools and systems; • Write structured, tested, readable and maintainable code, as well as participate in code reviews to ensure code quality and distribute knowledge for existing and new tools that allow us to extract, analyze, visualize and produce valued added insights of data for our customers; • Independently pursue research projects, implement novel computational workflows and support a dynamic multi-disciplinary team focused on moving projects from the hit identification stage to the discovery of candidates; • Perform statistical analysis and fin-tuning using test results; • Be actively involved in digital transformation projects and data governance objectives working closely with IT and scientific teams; • Ensure proper documentation of workflows, reports, tools, and analyses of the Research Informatics’ team; • Provide transparency and regular communication on project status, potential roadblocks for execution, and new strategies with Technology Sciences leadership; • Ensure project activities align with Eurofins policies, procedures and methodologies

• Design, develop, and implement AI/ML solutions to solve business problems, contributing to projects from proof of concept and pilot through production deployment. • Build and apply both traditional machine learning models and modern AI techniques, including large language models (LLMs), generative AI, retrieval-augmented generation (RAG), and agentic AI systems. • Collaborate with senior engineers, product managers, data scientists, and business stakeholders to refine project scope, technical approach, success metrics, and deployment requirements. • Contribute to building scalable and maintainable AI systems with a focus on accuracy, robustness, fairness, and explainability, adhering to responsible AI principles. • Apply model evaluation frameworks to assess performance, generalization, bias, and resilience to edge cases or adversarial inputs. • Support the transition of AI prototypes into production-ready systems by partnering with platform and engineering teams on deployment, monitoring, and lifecycle management. • Contribute to documentation, governance, and compliance processes to meet enterprise and regulatory standards, especially in healthcare and other regulated environments.