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.

• 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

Role Description This is a remote position. Shuru is a self-managed technology team specializing in accelerating visions through product, technology, and AI leadership. With a focus on bespoke execution, we deliver impactful solutions that are scalable and designed for success. At Shuru, we deliver mobile solutions that meet and exceed customer expectations. Our collaborative and fast-paced environment encourages creativity and innovation. We are hiring an MLOps Engineer for a leading workforce solutions company that is undergoing a major technology-led transformation. A key part of this transformation is the deployment of robust analytics and machine learning products that combine data, business knowledge, and enterprise goals to drive measurable business impact. The MLOps Engineer will play a critical role in building, deploying, and maintaining enterprise machine learning solutions. This role will focus on: - Designing scalable ML pipelines - Managing cloud-based ML environments - Supporting model governance - Ensuring high standards of model performance, reliability, and operational excellence You will work closely with data science, product, architecture, engineering, and business teams to bring machine learning solutions from development to production and support their continued improvement. As an MLOps Engineer, you will: - Work with stakeholders to define machine learning solution designs based on cloud services such as Azure and Snowflake. - Design, build, and maintain machine learning pipelines and frameworks to support enterprise analytics, reporting, and product needs. - Collaborate with data science, ML engineering, data quality, and product teams on model deployment architecture and implementation. - Manage, configure, and optimize cloud environments and related machine learning services. - Implement tools and processes for model integration, storage, profiling, monitoring, processing, management, and archival. - Support enterprise-wide model governance, performance tracking, and lifecycle management standards. - Recommend improvements to ML platforms, tools, and development practices to support strategic technology and business objectives. - Work with SaaS vendors and strategic partners to implement and maintain modern machine learning solutions. - Use Agile practices to manage delivery, contribute to project planning, and support successful rollout of ML products. - Partner with internal stakeholders to understand business requirements and translate them into reliable ML operations solutions. - Stay current with emerging MLOps tools, cloud technologies, and best practices to ensure solutions remain scalable, secure, and fit for purpose. Qualifications - Bachelor's degree in Computer Science, Engineering or Technical Field preferred. - Minimum 3-7 years of relevant experience. - Proven experience in machine learning engineering and operations. - Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration. - Execution of model deployment, monitoring, profiling, governance and analysis initiatives. - Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents. - Solid Python, ML frameworks (e.g., TensorFlow, PyTorch), data modeling, and programming skills. - Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP). - Experience using modern approaches to automating machine learning pipelines. - Agile and Waterfall methodologies. - Ability to work independently and manage multiple task assignments within a structured implementation methodology. - Personally invested in continuous improvement and innovation. - Motivated, self-directed individual that works well with minimal supervision. - Must have experience working across multiple teams/technologies. - Preferred but not essential: Experience with business intelligence tools (preferably PowerBI). - Preferred but not essential: Experience with MLOps tools (e.g., MLflow, Kubeflow). Benefits - Work on global projects with clients from worldwide. - Be part of a remote-first culture - work from anywhere with flexibility. - Enjoy team-building activities and regular outings. - Collaborate and grow in a supportive environment with opportunities to learn from senior engineers. - Competitive salary and benefits package.

• 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.