Engineering Manager - Commercial Data Science

7 - 12 years

10 - 14 Lacs

Posted:22 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Live | What you will do
Join Amgens Commercial Data & Analytics team to lead the development of scalable ML solutions for commercial decision-making.
  • Serve as the internal MLOps and technical enablement partner for data science teams across TA Delivery, Measurement, and Investment Analytics.
  • Lead and mentor a team of engineers, ensuring growth, skill development, and delivery of high-quality solutions
  • Design, build, and maintain reusable components to support modeling, experimentation, and insight delivery at scale.
  • Build robust data pipelines for ML and omnichannel promotional workflows, including data ingestion, transformation and validation
  • Implement and maintain ML pipelines in Databricks , including model registration, versioning, and production deployment via MLflow .
  • Validate and maintain processed data sources with schema enforcement, lineage tracking, and quality checks to ensure datasets are reliable for ML workflows.
  • Support advanced commercial data science use cases such as RAG pipelines, vector DB integrations, and document-based inference with well-documented, secure code.
  • Build lightweight, user-friendly interfaces (e.g., Streamlit ) for visualization or prototype delivery across analytic teams.
  • Collaborate with Amgens enterprise engineering, architecture, and cloud ops teams to follow best practices in AWS , S3 , and scalable compute.
  • Maintain clear documentation, version control, code reuse templates, and testing workflows to support robust and reusable deployment patterns.
  • Monitor deployed models for performance drift and retraining needs; establish visibility around pipeline health and telemetry.
  • Advise on connector usage, integration patterns, and technology decisions across CD&A analytics teams.

Basic Qualifications
  • Bachelors or masters in computer science, Software Engineering, Data Engineering, or related field.
  • 7 - 13 years of experience in MLOps, data engineering, or ML/AI product development.
  • Expertise in Python and SQL.
  • Prior experience using deep learning frameworks, including but not limited to, PyTorch, Tensorflow, Keras, etc.
  • Hands-on experience with Databricks and at least one major cloud platform (AWS, GCP, or Azure).
  • Experience building reproducible ML workflows using Git-based workflows, MLflow, and Delta Lake or similar tools.Strong foundation in software engineering best practices, including CI/CD pipelines, containerization, and workflow orchestration.
  • Demonstrated experience with model deployment, monitoring (drift, latency, error rates), and retraining workflows.
  • Ability to clearly document technical work and collaborate effectively with cross-functional teams, including data scientists, engineers, and platform teams.
  • Excellent communication skills and a willingness to communicate technical information to technical and non-technical audiences
  • Agile and adaptable to a fast-paced environment
Preferred Qualifications
  • Familiarity with RAG , vector databases , embedding pipelines , and LangChain-like frameworks.
  • Experience with low-code interface tools like Streamlit or Dash .
  • Experience optimizing ML pipelines and Spark/Databricks jobs for scale, reliability, and efficiency.
  • Ability to coach analysts and DS teams on technical patterns, integrations, and deployment best practices.
  • Strong interest in scaling DS enablement as a capability.
  • Strong problem-solving mindset, with an eye for developing reliable and scalable data engineering systems
  • Experience with pharma commercial analytics and commonly used data sources such as claims, physician level sales and promotional engagement data

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