Machine Learning Specialist

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Specialist based in United Kingdom.

This is a remote opportunity for an experienced Machine Learning Specialist to design and deliver data-driven products and AI solutions.

You will work across machine learning, data science, analytics, and predictive modeling to solve complex business challenges.

The role combines hands-on technical delivery with collaboration across data, engineering, analytics, and project teams.

You will transform large and complex datasets into actionable insights, models, reports, and intelligent applications.

There is also an opportunity to influence broader AI initiatives by providing technical guidance and helping shape analytical products.

The position places strong emphasis on responsible AI, including privacy, transparency, accountability, and ethical data practices.

It is well suited to a technically strong professional who enjoys turning advanced analytics into practical business outcomes.

• Design, develop, train, validate, and evaluate machine learning models for business and enterprise use cases.
• Analyze large and complex datasets to identify meaningful patterns, generate insights, and support data-driven decision-making.
• Prepare, clean, normalize, structure, and organize data for predictive and prescriptive modeling.
• Develop analytical models, data products, dashboards, reports, and visualizations that communicate findings effectively.
• Integrate machine learning models into applications, business processes, and operational workflows.
• Gather, clarify, and document business and technical requirements for AI and analytics initiatives.
• Collaborate closely with data engineers, analysts, developers, and project teams to deliver end-to-end data solutions.
• Provide technical leadership, guidance, and expertise across data science and AI initiatives.
• Support the development of full-stack analytics and AI applications when required.
• Promote responsible AI practices by considering privacy, accountability, transparency, security, and ethical implications throughout the development lifecycle.
• Identify, communicate, and appropriately elevate technical risks, dependencies, and issues within a multi-vendor environment.
• Develop and share reusable analytical models, methodologies, and data products to improve organizational capabilities.

• 6+ years of professional experience using statistical and programming languages for data analysis, machine learning, and related quantitative work.
• 6+ years of experience designing and implementing analytical and quantitative models.
• 6+ years of experience preparing and transforming data for predictive and prescriptive modeling.
• 6+ years of experience applying AI and machine learning techniques to real-world business or enterprise challenges.
• Strong understanding of data analysis methodologies, statistical techniques, predictive modeling, and machine learning concepts.
• Demonstrated ability to work with large, complex datasets and translate analytical results into practical recommendations.
• Strong programming and analytical capabilities, with the ability to develop robust, maintainable data and ML solutions.
• Experience collaborating effectively with multidisciplinary teams, including data engineers, analysts, developers, and project stakeholders.
• Ability to understand business requirements and translate them into appropriate technical and analytical approaches.
• Strong communication skills, with the ability to explain complex technical concepts clearly to both technical and non-technical audiences.
• A responsible and thoughtful approach to AI development, with awareness of privacy, transparency, accountability, and ethical AI principles.
• Ability to work independently, manage priorities, provide technical guidance, and elevate risks effectively in a complex delivery environment.

• Fully remote working arrangement.
• Opportunity to work on AI, machine learning, analytics, and data-driven enterprise initiatives.
• Exposure to complex business problems and large-scale datasets.
• Cross-functional collaboration with data, engineering, analytics, and project teams.
• Opportunity to provide technical leadership and influence the development of analytical data products.
• Environment focused on responsible, transparent, and ethical use of AI.