Data Scientist

As a Data Science/AI/ML professional at Cybage in Pune, you will have the opportunity to work with a leading company specialized in outsourced product engineering services. Cybage collaborates with top independent software vendors and utilizes a data-driven system called Decision Mines for Digital Excellence to provide innovative solutions leveraging modern technologies. Key Responsibilities: - Design, implement, and optimize ML pipelines with tools like TensorFlow, PyTorch, MLflow, Scikit-learn, or ONNX. - Operationalize models using CI/CD for ML practices (MLOps) on platforms like Kubeflow, Azure ML, or SageMaker. - Collaborate with data scientists to convert prototypes into production-level code. - Develop scalable data ingestion and transformation pipelines using technologies such as Apache Spark, Databricks, Pandas, or SQL. - Perform feature engineering and data cleansing to enhance model input. - Utilize data visualization tools like Plotly, Power BI, or Tableau for generating business insights. Qualifications Required: - Bachelor's or Master's degree in Computer Science, Data Science, or related fields. - Minimum of 3 years of experience in data science, ML engineering, or cloud-based systems. - Strong understanding of ML lifecycle, CI/CD, and cloud deployment strategies. - Previous experience in deploying AI models on the cloud. - Knowledge of data governance and ML security best practices. In addition, Cybage operates in multiple locations including GNR, Hyderabad, North America, Canada, UK, Europe, Japan, Australia, and Singapore, providing seamless services to clients across various industry verticals such as Media and Entertainment, Travel and Hospitality, Online Retail, Healthcare, SCM, and Technology. Please note that experience in Cloud & DevOps is considered a plus, where you will be involved in developing and deploying solutions on platforms like Azure, AWS, or Google Cloud Platform. You will also implement infrastructure as code using tools such as Terraform, Bicep, or CloudFormation to ensure high availability, security, and scalability of ML services through Docker, Kubernetes, and Helm.,