Data Scientist - MLOps & Analytics Governance
First seen by Alion on Sep 7, 2026.
Data Scientist - MLOps & Analytics Governance
Location: Bangalore
Experience: 4 - 8 Years
Job Summary:
We are looking for a technically strong Data Scientist - MLOps & Analytics Governance to drive data science, analytics governance, and AI/ML initiatives.
The role will focus on ensuring data quality, analytical accuracy, model governance, and reliable deployment of ML solutions, while working closely with business, product, data engineering, and domain teams.
The role requires strong analytical and stakeholder management capabilities, along with hands-on understanding of Python, SQL, cloud platforms, MLOps, statistical modeling, and data governance.
Key Responsibilities:
- Own and drive MLOps and analytics governance processes across data science initiatives.
- Establish and enforce data quality and governance standards across ML feature pipelines and training datasets.
- Ensure reliable model deployment, versioning, monitoring, and retraining on cloud platforms.
- Review and validate model outputs and analytical findings for accuracy, statistical soundness, bias, and reproducibility.
- Monitor data drift, prediction drift, and model performance and support corrective actions.
- Work with large-scale IoT sensor datasets from industrial equipment such as air compressors and rotating machinery.
- Support development of scalable time-series and fault-detection analytics.
- Work closely with data engineers, domain experts, product managers, and business stakeholders to translate requirements into scalable data science solutions.
- Communicate model performance, analytical insights, and business impact effectively to technical and non-technical stakeholders.
- Drive standardization, documentation, and governance of analytical processes.
- Leverage Generative AI tools such as GitHub Copilot, Claude, Cursor, or equivalent tools to improve development productivity and automate repetitive tasks.
Core Skills:
- 4 - 5 years of experience in Data Science, ML Engineering, Applied AI, or Analytics.
- Strong experience in MLOps, ML deployment, model monitoring, and governance.
- Strong Python and SQL skills with experience handling large-scale datasets.
- Experience with GCP Vertex AI or AWS SageMaker.
- Experience with BigQuery and scalable data processing.
- Strong understanding of data quality, data governance, data validation, and lineage.
- Knowledge of statistical modeling, including regression, classification, time-series forecasting, and hypothesis testing.
- Ability to assess data leakage, model bias, distributional shifts, and analytical reliability.
- Familiarity with IoT data architectures, streaming pipelines, and time-series databases.
- Experience with CI/CD, Git, model versioning, and Agile working environments.
- Proficiency with Generative AI coding assistants.
Preferred Skills:
- Experience in manufacturing, industrial IoT, predictive maintenance, or rotating equipment.
- Knowledge of air compressor systems and industrial machinery.
- Understanding of vibration, pressure, temperature, and flow parameters.
- Experience with InfluxDB, TimescaleDB, dbt, MQTT, or OPC-UA.
- Exposure to predictive maintenance and condition-based monitoring.
- Experience with cloud-based IoT ingestion services on GCP or AWS.
- MLOps or Cloud ML certifications such as Google Professional Machine Learning Engineer or AWS ML Specialty.
Qualification:
- B. Tech / M. Tech or equivalent technical qualification
Skills
Data Science, Data Scientist, Machine Learning, Analytics, Artificial Intelligence, SQL, Statistical Modeling, Data Analytics, Data Modeling