In this Lead Data Scientist role, you will drive end-to-end ML/AI projects from discovery to production, shaping Howden’s AI capabilities at scale. You’ll partner with business and engineering teams to identify opportunities, deliver production-grade solutions, and mentor others. You’ll act as a technical leader, setting best practices and helping translate complex analytics into business outcomes. This is a hands-on role with a strong business impact in a collaborative, employee-owned global organisation.
Pay / Benefits
reasonable adjustments
flexible hours
hybrid working
Responsibilities
Lead end-to-end delivery of ML/AI solutions from discovery to production and optimization
Collaborate with stakeholders to identify opportunities and prioritise based on business value
Design, develop, and deploy scalable ML, predictive analytics and AI applications
Advise on best practices in DS, experimentation, model development, MLOps and software engineering
Work with Data Engineering, Platform and Product teams to deliver production-grade solutions
Help evolve Howden's AI/data science capabilities, standards and ways of working
Support agile delivery teams with technical leadership throughout the product lifecycle
Stay current with DS/AI/cloud developments and introduce innovative approaches
Key requirements
Proven track record of building and deploying production-grade AI/ML applications with measurable business value
Experience collaborating with Data Engineering, MLOps and Product teams to deliver ML systems end-to-end
Excellent Python skills and hands-on use of XGBoost, PyTorch, TensorFlow or Hugging Face
Experience with cloud-based data/AI platforms, especially Azure and Databricks
Strong stakeholder engagement and ability to translate technical complexity into executive narratives
Experience in agile cross-functional product/delivery teams
Commercial mindset with focus on outcomes; insurance/financial services experience desirable
Experience with Generative AI, LLM applications, agentic systems
Mentoring/coaching experience for other data scientists or technical practitioners
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