Haleon
Consumer Health
SeniorMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer at Haleon. Skills: Machine Learning, ML Engineering, Python, Cloud. Lead ML solution deployment. Manage model lifecycle”
What You'll Achieve.
Transform integrated business planning process; Develop technical excellence
Industry & Context.
Excellent problem-solving skills
What They're Looking For.
Must Have
5-10 years of industry experience, proven experience implementing machine learning engineering pipelines on large datasets, understanding of ML engineering best practices, collaboration skills, comfortable working with ambiguity, making quick, informed decisions considering trade-offs, Experience with deploying machine learning solutions on enterprise infrastructure, programming skills in Python or similar programming languages, Ability to manage competing priorities across a portfolio of projects
Nice to Have
experience with cloud-based infrastructure and distributed computing systems, such as Azure, AWS, or GCP, Experience with containerization and orchestration tools, such as Docker and Kubernetes, Understanding of software engineering principles, such as modular design, clean code, and testing, Familiarity with DevOps practices and tools, such as continuous integration and deployment (CI/CD) pipelines and configuration management tools like Ansible or Terraform, Excellent problem-solving skills, ability to debug complex issues in production environments, communication skills, Familiarity with monitoring and logging tools, such as Grafana, Prometheus, and ELK Stack, Knowledge of security best practices in machine learning engineering
What You'll Do.
Lead ML solution deployment
Manage model lifecycle
Implement ML engineering best practices
Define and implement metrics
Coordinate competing priorities
Influence across organisations
Collaborate with cross-functional teams
Contribute to team standards
Motivate and mentor colleagues
How You'll Work.
Team & Collaboration
Proven collaboration skills; Collaborate with cross-functional teams; Collaborate with data scientists; Collaborate with data engineers; Collaborate with business stakeholders; Contribute to a highly collaborative team
Communication Scope
Communication skills
Process & Methodology
Manage competing priorities across a portfolio of projects
Full Job Description
Welcome to Haleon. We’re a purpose-driven, world-class consumer company putting everyday health in the hands of millions. In just three years since our launch, we’ve grown, evolved and are now entering an exciting new chapter – one filled with bold ambitions and enormous opportunity. Our trusted portfolio of brands – including Sensodyne®, Panadol®, Advil®, Voltaren®, Theraflu®, Otrivin®, and Centrum® – lead in resilient and growing categories. What sets us apart is our unique blend of deep human understanding and trusted science. Now it’s time to fully realise the full potential of our business and our people. We do this through our Win as One strategy. It puts our purpose – to deliver better everyday health with humanity – at the heart of everything we do. It unites us, inspires us, and challenges us to be better every day, driven by our agile, performance-focused culture. **About the role** This is an excellent opportunity for a Senior Machine Learning Engineer to provide key contribution to a strategic digital programme expected to transform the integrated business planning process in Haleon. The successful candidate will lead the deployment of machine learning solutions that will transform our integrated business planning process. In this role, you will be responsible for designing and implementing end-to-end machine learning pipelines for various applications. **Key Responsibilities of the role** * Lead the deployment of ML solutions on the enterprise infrastructure, in line with ML engineering standards and best practices (version control, testing, deployment, maintenance). * Manage the entire lifecycle of data science/machine learning models, including monitoring, data gathering for retraining, and updates. * Implement ML engineering best practices, such as coding standards, code reviews, and automated testing. * Define and implement metrics to evaluate the functional performance and computational resource efficiency of ML and AI components. * Can coordinate
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