Sr Manager, MLOps

Be part of something altogether life-changing!

Working at Cytiva means being at the forefront of providing new solutions to transform human health. Our incredible customers undertake life-saving activities ranging from fundamental biological research to developing innovative vaccines, new medicines, and cell and gene therapies.

At Cytiva you will be able to continuously improve yourself and us – working on challenges that truly matter with people that care for each other, our customers, and their patients. With associates across 40+ countries, Cytiva is a place where every day is a learning opportunity – so you can grow your career and expand your skills in the long term.

Forming part of the Biotechnology segment at Danaher, we bring together dedicated technical expertise and talent to develop the next generation of life-changing therapeutics.

The Sr Manager, MLOps for Cytiva is responsible for leading a new machine learning operations team. The ideal candidate will have a strong background in machine learning operations, a passion for deploying scalable and reliable machine learning solutions, and proven leadership skills. This role will involve close collaboration with data scientists, engineers, and stakeholders across our business to ensure seamless integration of ML models into production environments.

What you will do:

• Define and implement the MLOps strategy, including the integration of tools and platforms to support model operations.
• Lead, mentor, and manage a team of MLOps engineers who will oversee the end-to-end lifecycle of machine learning models, from development to deployment and monitoring.
• Develop and maintain CI/CD pipelines for ML models.
• Implement best practices for model versioning, monitoring, security and governance.
• Ensure scalability and reliability of ML solutions in production.
• Foster a culture of collaboration, continuous improvement, and innovation within the team and implement team goals and performance metrics.

Who you are:

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field. Master's or PhD preferred.
• 5+ years of experience in machine learning, data science, or related fields and 3+ years of experience in a leadership role, managing and mentoring technical teams.
• Proven experience with MLOps tools and frameworks (e.g., Valohai, MLflow, Kubeflow, Airflow).
• Strong background in deploying and managing ML models in production environments.
• Experience in implementing automated retraining and model optimization in live environments.
• Knowledge of strategies to manage model drift and ensure model performance over time.
• Excellent problem-solving and analytical skills.
• Strong communication and interpersonal skills.

At Cytiva we believe in designing a better, more sustainable workforce. We recognize the benefits of flexible, hybrid working arrangements for eligible roles and are committed to providing enriching careers, no matter the work arrangement. This position is eligible for a hybrid work arrangement in which you can work part-time at the Company location identified above and part-time remotely from your home. Additional information about this hybrid work arrangement will be provided by your interview team. Explore the flexibility and challenge that working for Cytiva can provide.

At Danaher we bring together science, technology and operational capabilities to accelerate the real-life impact of tomorrow’s science and technology. We partner with customers across the globe to help them solve their most complex challenges, architecting solutions that bring the power of science to life. Our global teams are pioneering what’s next across Life Sciences, Diagnostics, Biotechnology and beyond. For more information, visit www.danaher.com.

At Danaher, we value diversity and the existence of similarities and differences, both visible and not, found in our workforce, workplace and throughout the markets we serve. Our associates, customers and shareholders contribute unique and different perspectives as a result of these diverse attributes.