Machine Learning Engineer / Data Scientist
Datamatics Technologies (company site, opens in a new tab)6+ yrsMumbaiOn-siteDirect career pageEst. 18–30 LPA
Posted 2d ago
Reach people at Datamatics Technologies
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Datamatics Technologies you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, validate, deploy, and monitor machine learning models for business and operational use cases in a complex enterprise environment.
- Build reproducible ML pipelines and operationalize models using enterprise data platforms with end-to-end ML solution delivery.
- Integrate models into business workflows and ensure continuous monitoring, retraining, and versioning of models.
Minimum Requirements
- 6+ years of data science or machine learning experience, including 3+ years of hands-on model development and production deployment.
- Advanced Python skills with experience in pandas, NumPy, scikit-learn, and PyTorch or TensorFlow.
- Hands-on experience with Azure Machine Learning platform including notebooks, pipelines, model registry, deployment, and monitoring.
- Must be available for onsite work in Abu Dhabi and able to start as soon as possible; English language required, Arabic speaking preferred.
Ideal Candidate Profile
- Proven ability to deliver end-to-end ML solutions within large, complex enterprise environments.
- Strong ML Ops experience with tools like ML flow, Git, Azure DevOps, CI/CD, Docker, and APIs, indicating operational deployment focus.
- Familiarity with advanced ML techniques (regression, classification, time-series, anomaly detection) and domain application in operational or forecasting contexts.