Manager, Data Science, AI & Data, AI Scaling & Transformation

Job description

As a practitioner in AI & D, you are responsible for delivering Data Science on client projects. You are encouraged to devise innovative solutions to help our clients address their biggest data challenges including developing modern analytics platforms.

• Apply data science, machine learning and AI techniques creatively to solve complex problems.
• Research and develop innovative solutions that deliver real-world impact.
• Pioneer implementation of new techniques and technologies.
• Participate in all phases of the lifecycle from capturing user needs and developing prototypes to scaling and managing live services.
• Be responsible for the high‑quality delivery of projects.
• Shape new propositions and support winning new projects.
• Support continual learning and development in the team.

• Hands‑on experience of applying data science or machine learning.
• Understanding of a range of machine learning architectures and models (e.g. Transformers, CNNs, Generative AI) and understand their applicability to client use cases.
• Strong communication skills, including preparing engaging and impactful reports and presentations and conveying complex issues to diverse audiences.
• Leading data teams to deliver high quality AI & Data projects for clients.
• Building productive relationships with colleagues and clients.
• Evidence of contribution to practice development, including developing new propositions, mentoring colleagues and supporting bid work.

• Experience applying state‑of‑the‑art techniques to derive insights from varied data sources.
• Experience with Data Science and Machine Learning services and toolkits from Cloud providers (AWS, Azure, GCP) or equivalent.
• Practical experience with Python and good practice tools and processes for developing high‑quality code.
• Familiarity with common Python libraries for data management, statistical analysis, machine learning.
• Experience with popular machine learning frameworks such as Tensor Flow and PyTorch.
• Strong understanding of statistics and probability.
• Strong knowledge of the machine learning lifecycle, from prototyping state‑of‑the‑art to scaling and managing for enterprise use.
• Experience with one or more common workflow / pipelining frameworks (Kubeflow, MLFlow, Argo or equivalents).
• Understanding of key considerations for Ethical and Responsible AI and experience applying them.

Manchester (hybrid working).