Lead Machine Learning Engineer

Overview

As Lead Machine Learning Engineer in Kingfisher’s Data Science team, you will drive the development of ML/AI services across the group, building and guiding a high-performing team. You will partner with tech, product and data groups to embed data science into platforms and products, translating business needs into data-driven solutions. You will champion data science adoption, help nurture a data culture, and elevate the data science brand internally and externally. This is a chance to shape scalable AI products at a global home-improvement leader.

Pay / Benefits

• flexible and agile working hours
• inclusive environment
• opportunities to stretch and grow career
• competitive benefits package

Responsibilities

• Lead the implementation of data science projects to support commercial goals
• Develop and lead a high-performing ML engineering team
• Collaborate with tech, product and data teams to build data platforms for data science integration
• Bridge business and data teams to translate requirements and communicate results
• Champion data science across the Kingfisher group
• Support the data leadership in advancing a data-driven decision culture
• Drive the data science and customer analytics brand within and outside Kingfisher

Key requirements

• Proven experience delivering AI-based products and productionising ML-based solutions
• Cloud-based ML services experience (preferably GCP)
• Strong knowledge of classical ML (Logistic Regression, Random Forest, XGBoost) and modern DL (BERT, LSTM)
• Strong SQL and Python data-analysis ecosystem skills (Jupyter, Pandas, Scikit-Learn, Matplotlib)
• Strong software development skills (Python)
• Experience deploying ML/AI services using Kubernetes & Kubeflow
• Solid management/leadership experience and stakeholder management
• Customer-focused mindset and cross-functional collaboration abilities

• Leadership and people management
• Excellent communication and stakeholder management
• Collaborative mindset and cross-functional teamwork
• AI-based product development
• Cloud ML deployment (GCP)
• Classical ML algorithms

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