Version 1
Information Technology And Services
Sr.Machinelearning/DataScientist
Neural analysis suggests this role is
optimal for mid candidates.
“Sr. Machine learning /Data Scientist at Version 1. Skills: Machine Learning, Deep Learning, Software Development, MLOps. Design, build, and deploy machine learning solutions. Apply traditional ML and deep learning techniques”
What You'll Achieve.
deliver measurable outcomes; real world delivery; measurable outcomes; model performance; data drift
Industry & Context.
problem-solving mindset; structured thought process; reason through ambiguous requirements; iterate effectively
What They're Looking For.
Must Have
5+ years in applied machine learning and deep learning roles, Probability & statistics, hypothesis testing, bias/variance, regularisation, Optimisation, gradient-based methods, loss functions, evaluation metrics, Linear algebra fundamentals, vectors/matrices, decompositions, traditional ML modelling, feature engineering, model selection, validation, error analysis, deep learning, architectures, training dynamics, evaluation, build good quality software, clean code, testing, debugging, maintainable design, programming skills, integrating ML into production systems, problem-solving mindset, structured thought process, reason through ambiguous requirements, iterate effectively, delivering ML solutions end-to-end, prototyping, validation, production/operations, Databricks, Spark, MLflow, model lifecycle, MLOps frameworks, deep learning frameworks, PyTorch, GenAI / LLMs, AWS Bedrock, AWS SageMaker, SQL, data analysis skills
Nice to Have
Experience in regulated or security conscious environments, model governance, monitoring, managing model performance, production model deployment patterns, Responsible AI, model governance, Client facing or consulting experience, AWS certifications, Microsoft certifications, Oracle certifications, Red Hat certifications
What You'll Do.
and deploy machine learning solutions
Apply traditional ML and deep learning techniques
Demonstrate ML fundamentals
Develop and deploy ML and data science solutions
Perform data exploration
and model development
Collaborate with data engineers and AI engineers
Continuously improve models
How You'll Work.
Team & Collaboration
working closely with engineers and stakeholders; Collaborate with data engineers and AI engineers
Communication Scope
explain trade-offs clearly
Full Job 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. We’re an award-winning employer reflecting how our employees are at the very heart of what we do: * 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 We’re a core values driven company, we hire people who share our values, and we reward those who display and foster them, it’s deeply embedded within our DNA. Invest in us and we’ll invest in you. Role Overview We are seeking a Data Scientist to who combines strong machine learning fundamentals with the ability to build high‑quality, production-ready software. You’ll take ML solutions from problem framing and modelling through to robust implementation, deployment, and iteration, working closely with engineers and stakeholders to deliver measurable outcomes for enterprise and regulated clients. 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 al
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