Data Scientist – Azure ML / MLOps

About the Role

Role Overview We are looking for a hands-on Data Scientist with strong Azure Machine Learning and MLOps experience to work on data science and machine learning initiatives. The candidate will be responsible for developing, deploying, monitoring, and maintaining ML models in an Azure cloud environment. The ideal candidate should have a strong foundation in Python, Machine Learning, Azure ML Studio, and MLOps, with the ability to take models from experimentation through production deployment.

Responsibilities

• Develop and implement machine learning models using Python and standard ML frameworks.
• Perform data exploration, feature engineering, model development, validation, and optimization.
• Build and manage ML workflows using Azure Machine Learning / Azure ML Studio.
• Develop scalable and repeatable MLOps pipelines for model training, deployment, and monitoring.
• Deploy ML models into production using Azure ML endpoints and related Azure services.
• Implement CI/CD practices for machine learning model and pipeline deployment.
• Monitor model performance, data drift, and model health in production.
• Collaborate with Data Engineers, Cloud Engineers, Architects, and Business stakeholders.
• Troubleshoot model and pipeline issues and continuously improve ML solutions.
• Ensure solutions follow enterprise standards for security, governance, scalability, and reliability.

Required Skills

• Mandatory Technical Skills
• 5–8 years of experience in Data Science / Machine Learning.
• Strong hands-on experience with Python.
• Strong understanding of Machine Learning algorithms and statistical concepts.
• Hands-on experience with Azure Machine Learning / Azure ML Studio.
• Good experience in MLOps concepts and implementation.
• Experience building ML pipelines for training, validation, deployment, and monitoring.
• Experience with Git and CI/CD.
• Hands-on experience with libraries/frameworks such as Scikit-learn, Pandas, NumPy and preferably PyTorch/TensorFlow.
• Good understanding of model deployment, versioning, monitoring, and lifecycle management.

Preferred Skills

• Good to Have
• Experience with Azure DevOps.
• Exposure to Azure Data Factory, Databricks, Azure Storage, Azure Functions or related Azure services.
• Experience with Docker / Kubernetes.
• Experience with REST APIs and model serving.
• Exposure to LLM / GenAI / NLP use cases.
• Experience working in regulated domains such as Insurance or Financial Services.

Candidate Profile

We are looking for candidates who are hands-on practitioners, rather than candidates with only theoretical Data Science knowledge. The candidate should be able to independently work across the ML lifecycle:

• Data → Feature Engineering → Model Development → Azure ML → MLOps Pipeline → Deployment → Monitoring

Strong problem-solving, communication, and stakeholder-management skills are expected.