Seagate is offering an internship position for the role of Intern – Data Science at their Pune location. This opportunity is open to graduates from any discipline who have experience with machine learning frameworks and tools such as sci-kit-learn, TensorFlow, or PyTorch. Additionally, candidates should be familiar with machine learning lifecycle platforms like Kubeflow and MLflow, as well as cloud data services such as GCP and AWS. This internship provides a valuable chance to gain hands-on experience in the field of data science within a leading technology company.
Company Name: Seagate
Job Role: Intern – Data Science
Job Type: Internship
Education Required: Any Graduate
Skills Required: Experience with machine learning framework tools like sci-kit-learn, TensorFlow, or PyTorch
Experience with machine learning lifecycle platforms (Kubeflow, MLflow) and cloud data services (GCP, AWS)
Job Location: Pune
Role and Responsibilities:
- Assist with implementing end-to-end ML pipelines and deliver scalable production Machine learning services.
- Collaborate with machine learning engineers to build scalable data pipelines and development of new ML models.
- Leverage MLops tools to support ML workflows including experimentation, training, and production operations.
- Design and implement tools and processes for the entire ML Ops including model development, model deployment, model serving, model monitoring, and experimentation.
- Work on multiple streams of analytics which include Time series, image, and Tabular data
- Work on building accelerators and ML frameworks.
Required Skills and Qualifications:
- Experience with machine learning frameworks tools like sci-kit-learn, TensorFlow, or PyTorch.
- Experience with machine learning lifecycle platforms (Kubeflow, MLflow) and cloud data services (GCP, AWS)
- Experience in at least one major programming language (e.g. Python, Scala, Java) and SQL (any variant)
- A collaborative coder, comfortable with Git and code reviews.
- Good Knowledge of data management and data mining architecture.
- Ability to proactively identify and resolve issues.
