Lead Machine Learning Engineer

October 23

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Williams Lea

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Enterprise • B2B • SaaS

Williams Lea is a global provider of business and administrative support services, specializing in presentations, creative services, secretarial support, document and word processing, billing, collections, and more. They offer tailored services to industries such as legal, financial, and professional services firms, aimed at enhancing operational efficiency and cost reduction. Their ENGAGE technology enables companies to leverage AI for improved service quality and performance in a virtual and digital environment. Operating worldwide, Williams Lea supports businesses with digitized, high-touch, and connected services designed for hybrid workforces in regulated environments.

5001 - 10000 employees

🏢 Enterprise

🤝 B2B

☁️ SaaS

📋 Description

• Maintain and expand the enterprise data model • Develop, publish, and maintain business-critical reports for stakeholders • Collaborate with the Data & Analytics team, business stakeholders, and subject matter experts • Lead the implementation of ML solutions on AWS cloud • Manage cloud-based platforms and optimize performance using Infrastructure as Code

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field • 5+ years of hands-on experience in machine learning or data science roles • Advanced programming skills in Python (including libraries such as pandas, scikit-learn, TensorFlow/PyTorch) • Solid understanding of ML algorithms, model evaluation techniques, and optimisation • Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker • Strong knowledge of DevOps/MLOps practices • Ability to thrive in a hybrid work environment • Excellent problem-solving and analytical thinking skills • Strong communication skills

🏖️ Benefits

• 25 days holiday, plus bank holidays (pro-rata for part time roles) • Salary sacrifice schemes, retail vouchers • Life Assurance • Private Medical Insurance • Dental Insurance • Health Assessments • Cycle-to-work scheme • Discounted gym memberships • Referral Scheme