ML Engineer, Consumer Analytics

at Eli Lilly and Company
Published December 13, 2022
Location Bengaluru, India
Category Machine Learning  
Job Type Full-time  


At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our 35,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Eli Lilly Services India Pvt Ltd

Consumer Marketing Analytics Team: ML Engineer

As Eli Lilly strives to achieve its purpose of making life better for patients, we have been building up our in-house ‘Consumer Experience’ function, which will design and execute next-generation marketing campaigns aimed at informing and educating consumers (or patients) directly.

To support the Consumer marketing teams in their decision-making, a data and analytics team has been set up simultaneously in Indianapolis (HQ) and Bengaluru (LCCI). This team is responsible for setting up the data warehouses necessary to handle large volumes of digital streaming data, create meaningful analyses using that data, and deliver recommendations to leadership.

As part of the LCCI team, we are excited to offer the role of a ML Engineer who will be an integral part of the Consumer analytics team.

Core Responsibilities

  • 4-8 years of demonstrated experience of building and deploying ML pipelines
  • Take standalone models built by data scientists and enhance them to build large scale machine learning pipelines that are deployed on the cloud (AWS)
  • Design and build robust and optimized ML pipelines by applying software engineering rigor and best practices to machine learning, including CI/CD, automation, etc
  • Coordinate with diverse stakeholders such as statisticians, software engineers, infrastructure teams to better understand requirements and constraints to design the most optimal ML pipelines
  • Continuous learning to stay up to date with new technologies to improve performance, maintainability, and reliability of ML systems
  • Solving open ended and unstructured questions in an environment where new ML pipeline solutions need to be explored and built from the ground up


  • Deep expertise in building ML/automation pipelines from scratch
  • Strong knowledge of AWS Sagemaker and working knowledge of AWS EC2
  • Strong knowledge of python and PySpark. Working knowledge of R is preferred
  • Understanding of statistical modelling concepts highly preferred
  • Strong knowledge of working tools like Docker, Kubernetes, Jenkins


  • Bachelor’s degree or master’s degree in technology, Statistics or Computer Science background preferred but not mandatory

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