Analyst – Data Engineer

About the job

Analyst – Data Engineer Job Title: Analyst – Data Engineer, MLOPs Grade: C3 Shift: 2 pm to 11 pm IST Role: Individual contributor role. Location: Manyata Tech Park, Bangalore. Introduction to role: The Data Engineer/MLOps 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 lifecycle—from 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 guidelines 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 stakeholders; provide frameworks, templates, and guardrails; conduct training and code reviews to support engineering best practices. • Documentation & Knowledge Sharing: Develop clear technical documentation, operational playbooks, and user guides for models, pipelines, and platform components. Essential Skills/Experience: • Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field, or equivalent experience. • Experience: 3–6+ years in MLOps, Data Engineering, or ML Platform roles, with a proven track record of deploying ML solutions at scale. • Programming: Proficiency in Python; familiarity with SQL; 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 cross-functional collaboration. When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility.