Lead ML Engineer: Data Platforms & AI Innovation

Kingfisher; City of Westminster; Hybrid; GBP 120,000 - 160,000; Full time

Job summary

Kingfisher is seeking a Lead Machine Learning Engineer to spearhead the research and development of ML/AI services within the Group Data Science team. You will trailblaze the development of data science algorithms while building, leading, nurturing and retaining a high-performing data science team across banner and group priorities.

You will partner with technology, product and data teams to embed data science in our platforms and processes and to drive a strong data-driven culture across the

Qualifications

• Proven experience delivering AI-based products and productionising ML/AI services.
• Proven experience developing cloud-based ML services (preferably GCP).
• Excellent understanding of classical ML algorithms and modern deep learning approaches.
• Strong software development skills, primarily in Python.

Responsibilities

• Lead the implementation of data science projects to support commercial goals.
• Develop and mentor a high-performing team of Machine Learning Engineers.
• Collaborate with tech, product and data teams to build data platforms enabling data science in products and processes.
• Translate business needs into data-centric project designs and communicate results clearly.
• Champion the application of data science across the Kingfisher group.
• Support the data leadership in promoting a data-driven culture and measurable value.

Skills

Leadership

Cloud ML

ML Algorithms

Python

Kubernetes

SQL

Tools

Kubernetes

Kubeflow

GCP

Python ecosystem

Job description

Kingfisher is seeking a Lead Machine Learning Engineer to spearhead the research and development of ML/AI services within the Group Data Science team. You will trailblaze the development of data science algorithms while building, leading, nurturing and retaining a high-performing data science team across banner and group priorities.

You will partner with technology, product and data teams to embed data science in our platforms and processes and to drive a strong data-driven culture across the