Analyst Data Science – MLOps Engineer

Job Title:Analyst - Data Science MLOps Engineer

Career Level:C3

Shift:2 pm to 11 pm IST

Location:Manyata Tech Park Bangalore.

Introduction to role:

The Data Science MLOps Engineer role collaborates with data scientists analysts and commercial operations to design deploy and manage machine learning systems that enhance sales effectiveness and engagement. This position is responsible for ensuring that models are reproducible compliant performant and scalable throughout their lifecyclefrom experimentation to production. A strong focus is placed on data quality monitoring and governance.

Accountabilities:

• Model Lifecycle Management: Develop and maintain pipelines to transition models from experimentation to production including packaging CI/CD automated testing and deployment across multiple environments.
• Data Pipeline Development: Design robust batch and streaming data workflows; define and manage feature sets lineage and reuse to support AI/ML initiatives.
• Production Operations: Ensure reliability and scalability of ML systems; manage incident response on-call support and implement effective logging tracing and alerting.
• Monitoring & Observability: Establish comprehensive monitoring for model performance data drift bias and service health; set thresholds create dashboards and automate remediation processes.
• Model Governance & Compliance: Implement version control approvals documentation and audit trails for datasets code models and experiments; ensure compliance with privacy regulations (HIPAA/PHI GDPR) and support AZ IT compliance requirements as needed.
• Experiment Management: Standardize experiment tracking and artifact management; promote standard processes for feature engineering model packaging and dependency management.
• Release & Change Management: Coordinate releases with commercial operations and IT; maintain runbooks rollback strategies change tickets and release notes in alignment with enterprise processes.
• Security & Access Controls: Enforce secrets management role-based access control network policies and data protection for sensitive healthcare and commercial data.
• Cost Optimization: Monitor and optimize cloud and computing costs for training inference and data movement; select architectures that balance performance with budget constraints.
• Collaboration & Enablement: Work closely with data scientists and business partners; provide frameworks templates and guardrails; conduct training and code reviews to support engineering standards.
• Documentation & Knowledge Sharing: Develop clear technical documentation operational playbooks and user guides for models pipelines and platform components.

Essential Skills/Experience:

• Education: Bachelors or Masters degree in Computer Science or Data Science or ML Engineering or a related field or equivalent experience.
• Experience: 36 years in ML engineering or MLOps or Data Science with MLOps or ML Platform roles with a proven track record of deploying ML solutions at scale.
• Programming: SQL Python orchestration data science pipelines end-to-end ML frameworks (ex: scikit-learn TensorFlow PyTorch) PySpark (preferred for future needs) Airflow DBT Snowflake MLflow experience with unit and integration testing and code quality standards.
• CI/CD & Infrastructure: Experience with CI/CD tools (e.g. GitHub Actions Azure DevOps) and containerization (Docker).
• ML Tools: Hands-on experience with model packaging and serving frameworks (e.g. SageMaker Azure ML) and experiment tracking tools.
• Data Technologies: Proficiency with distributed processing (Spark) data orchestration (Airflow) and cloud data services (e.g. Azure Data Lake Snowflake AWS S3).
• Security & Compliance: Understanding data privacy and security in healthcare; experience with secrets management and audit controls.

Desirable Skills/Experience:

• Domain Experience: Knowledge of pharmaceutical commercial analytics (HCP/HCO targeting call planning incentive compensation demand forecasting omnichannel measurement).
• Performance & Scalability: Experience with high-throughput inference batch scoring at scale and low-latency APIs.
• Workflow Reliability: Skills in incident management and capacity planning for ML systems.
• Automation & Templates: Ability to create reusable pipelines and starter kits for rapid project onboarding.
• Communication: Excellent verbal and written communication skills; able to present complex findings to both technical and non-technical audiences.
• Team Collaboration: Strong orientation toward teamwork and multi-functional collaboration.

At AstraZenecas Alexion division we champion diversity and foster an energizing culture where new ideas thrive. Our commitment to inclusion ensures that life-changing innovations can come from anywhere. We celebrate each others successes and take pride in giving back to our communities. Here your career is more than just a path; its a journey to making a difference where it truly counts.

Ready to make a difference Apply now to join our team!

We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race religion color national origin sex gender gender expression sexual orientation age marital status veteran status or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process to perform essential job functions and to receive other benefits and privileges of employment. Please contact us to request accommodation.

Date Posted

Closing Date

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