Haleon - Senior Machine Learning Engineer

2 weeks ago

Bangalore, India Haleon Full time

Hello. We're Haleon. A new world-leading consumer health company. Shaped by all who join us. Together, we're improving everyday health for billions of people. By growing and innovating our global portfolio of category-leading brands - including Sensodyne, Panadol, Advil, Voltaren, Theraflu, Otrivin, and Centrum - through a unique combination of deep human understanding and trusted science. What's more, we're achieving it in a company that we're in control of. In an environment that we're co-creating. And a culture that's uniquely ours. Care to join us. It isn't a question.

With category leading brands such as Sensodyne, Voltaren and Centrum, built on trusted science and human understanding, and combined with our passion, knowledge and expertise, we're uniquely placed to do this and to grow a strong, successful business. This is an exciting time to join us and help shape the future. It's an opportunity to be part of something special.

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 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 and manage competing priorities across a portfolio of projects.


- Ability to influence across organisations, proven collaboration skills, comfortable working with ambiguity, collaborate with cross-functional teams, including data scientists, data engineers, and business and Team management :


- Contribute to a highly collaborative team with a culture of ownership, initiative and responsibility.


- Contribute to the development of our Team's ML engineering standards of reusable DS, ML, and AI assets.


- Motivate, coach, mentor colleagues within the ML Engineering Team to develop technical excellence.


- Manage a team of up to 3 Machine Learning Engineers


Necessary Qualifications & Skills :


- BSc, MSc or PhD degree in mathematics, computer science, or another scientific discipline that provides solid foundations on relevant aspects of Data Science.


- 5-10 years of industry experience with proven experience implementing machine learning engineering pipelines on large datasets.


- Strong understanding of ML engineering best practices.


- Strong collaboration skills and comfortable working with ambiguity, making quick, informed decisions considering trade-offs.


- Experience with deploying machine learning solutions on enterprise infrastructure.


- Strong programming skills in Python or similar programming languages..


- Ability to manage competing priorities across a portfolio of desirable Qualifications & Skills :


- Strong 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 and the ability to debug complex issues in production environments.


- Strong communication skills and the ability to collaborate with cross-functional teams.


- Familiarity with monitoring and logging tools, such as Grafana, Prometheus, and ELK Stack.


- Knowledge of security best practices in machine learning engineering.

(ref:hirist.tech)

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