Sr. Data Scientist

Job Details

Job Description:

Join Altera, a leader in programmable logic technology, as we strive to become the #1 FPGA company. We are looking for a skilled and experienced Sr. Data Scientist to lead the development and deployment of production-grade ML pipelines and infrastructure across the enterprise. This is a highly technical, hands-on role focused on building scalable, secure, and maintainable machine learning solutions within the Azure ecosystem.

As a senior member of our Data & Analytics team, you will work closely with other data scientists, ML specialists, and engineering teams to operationalize ML models using modern tooling such as Azure Machine Learning, Dataiku, and Kubeflow, etc. You’ll drive MLOps practices, automate workflows, and help build a foundation for responsible and reliable AI delivery.

Responsibilities

• Design, build, and maintain automated ML pipelines from data ingestion through model training, validation, deployment, and monitoring using Azure Machine Learning, Kubeflow, and related tools.
• Deploy and manage machine learning models in production environments using cloud-native technologies like AKS (Azure Kubernetes Service), Azure Functions, and containerized environments.
• Partner with data scientists to transform experimental models into robust, production-ready systems, ensuring scalability and performance.
• Drive best practices for model versioning, CI/CD, testing, monitoring, and drift detection using Azure DevOps, Git, and third-party tools.
• Work with large-scale datasets from enterprise sources using Azure Synapse Analytics, Azure Data Factory, Azure Data Lake, etc.
• Build integrations with platforms like Dataiku etc. to support collaborative workflows and low-code user interactions while ensuring underlying infrastructure is robust and auditable.
• Set up monitoring pipelines to track model performance, ensure availability, manage retraining schedules, and respond to production issues.
• Write clean, modular code with clear documentation, tests, and reusable components for ML workflows.
• Support and mentor junior engineers and data scientists in engineering best practices and MLOps principles.
  
  

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 10+ years of hands-on experience developing and deploying machine learning models in production environments.
• Strong programming skills in Python, with experience in ML libraries such as scikit-learn, TensorFlow, PyTorch, or XGBoost.
• Proven experience with the Microsoft Azure ecosystem, especially:
• Azure Machine Learning (AutoML, ML Designer, SDK)
• Azure Synapse Analytics and Data Factory
• Azure Data Lake, Azure Databricks
• Azure OpenAI and Cognitive Services
• Experience with MLOps frameworks such as Kubeflow, MLflow, or Azure ML pipelines.
• Familiarity with CI/CD tools like Azure DevOps, GitHub Actions, or Jenkins for model lifecycle automation.
• Experience working with APIs, batch and real-time data pipelines, and cloud security practices.
  

Preferred Qualifications:

• Experience with tools like Dataiku for operationalizing ML workflows.
• Exposure to model observability tools (e.g., SHAP, LIME, Evidently).
• Knowledge of containerization tools (Docker, Kubernetes) and infrastructure-as-code (Terraform, Bicep).
• Background in industries with complex operational data (e.g., manufacturing, finance, supply chain).
• Azure certifications such as Azure Data Scientist Associate or Azure AI Engineer Associate.
  
  

Why Join Us?

• Build and scale real-world ML systems on a modern Azure-based platform.
• Help shape the AI and ML engineering foundation of a forward-looking organization.
• Work cross-functionally with experts in data science, software engineering, and operations.
• Enjoy a collaborative, high-impact environment where innovation is valued and supported.
  
  

Job Type

Regular

Shift

Shift 1 (India)

Primary Location:

Ecospace 1

Additional Locations:

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.