Data Scientist

About NetApp

NetApp is the intelligent data infrastructure company, turning a world of disruption into opportunity for every customer. No matter the data type, workload or environment, we help our customers identify and realize new business possibilities. And it all starts with our people.

If this sounds like something you want to be part of, NetApp is the place for you. You can help bring new ideas to life, approaching each challenge with fresh eyes. Of course, you won't be doing it alone. At NetApp, we're all about asking for help when we need it, collaborating with others, and partnering across the organization - and beyond.

We are looking for a talented and motivated ML Ops Engineer to provide technology leadership in building and maintaining robust machine learning operations (ML Ops) frameworks.

This role will focus on enabling scalable, reliable, and efficient deployment of AI/ML models, ensuring seamless integration between Data Science and production systems.

You will collaborate closely with Data Scientists, ML Engineers, and DevOps teams to operationalize cutting-edge machine learning solutions and optimize the end-to-end ML lifecycle.

• Design, implement, and maintain ML Ops pipelines for model training, deployment, monitoring, and retraining.
• Collaborate with Data Science teams to transition models from research to production.
• Automate workflows for data ingestion, feature engineering, and model evaluation.
• Ensure scalability, reliability, and performance of deployed ML systems.
• Implement monitoring tools to track model performance and detect drift.

• Stay informed about best practices in ML Ops, Data Science, and cloud-native deployments.

• 3–5 years of experience in Data Science and Machine Learning, with strong exposure to ML Ops practices.

• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with ML Ops tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, etc.).
• Strong understanding of CI/CD pipelines and containerization (Docker, Kubernetes).
• Knowledge of cloud platforms (AWS, Azure, GCP) for ML deployment.
• Familiarity with data processing tools (Spark, Pandas, etc.).

• Bachelor's or Master's degree in Computer Science, Data Science, or related field.
• Experience with monitoring and logging tools for ML models.
• Exposure to Generative AI model deployment (optional but nice to have).

At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in-office and/or in-person expectations, which will be shared during the recruitment process.

Equal Opportunity Employer:

NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all laws that prohibit employment discrimination based on age, race, color, gender, sexual orientation, gender identity, national origin, religion, disability or genetic information, pregnancy, and any protected classification.

Why NetApp?

We are all about helping customers turn challenges into business opportunity. It starts with bringing new thinking to age-old problems, like how to use data most effectively to run better - but also to innovate. We tailor our approach to the customer's unique needs with a combination of fresh thinking and proven approaches.

We enable a healthy work-life balance. Our volunteer time off program is best in class, offering employees 40 hours of paid time off each year to volunteer with their favourite organizations. We provide comprehensive benefits, including health care, life and accident plans, emotional support resources for you and your family, legal services, and financial savings programs to help you plan for your future. We support professional and personal growth through educational assistance and provide access to various discounts and perks to enhance your overall quality of life.

If you want to help us build knowledge and solve big problems, let's talk.

Submitting an application

To ensure a streamlined and fair hiring process for all candidates, our team only reviews applications submitted through our company website. This practice allows us to track, assess, and respond to applicants efficiently. Emailing our employees, recruiters, or Human Resources personnel directly will not influence your application.