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ML Ops Analyst Intern

Full Time On Site (Full Time) @Eli Lilly and Company in Data Science
  • Bengaluru, Karnataka, India View on Map
  • Post Date : October 26, 2021
  • Apply Before : November 25, 2021
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Job Description

About the job

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.

LCCI OmniChannel Execution: ML Operations Analyst (Intern)

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 39,000 employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease. We’re looking for people who are determined to make life better for people around the world

As Lilly strives to achieve these objectives, we have embarked on building and in-housing a state-of-the-art “recommendation engine” platform to enable more agile sales and marketing operations. This platform will

  • ingest a wide variety of data sources,
  • run advanced personalization models
  • and integrate seamlessly with all other Lilly operations platforms

to provide recommendations on “what should we do next” to the sales and marketing teams at the individual doctor level, enabling better decision making and improving customer experience.

Core Responsibilities

As part of this team, we are looking for analysts who will use variety of data (including customer 360, market and product sales, prior channel activity and affinities etc) to maintain, update, assess and fine tune the performance of the underlying machine learning algorithms that drive personalized recommendations for all key brands for Lilly in the US.

  • Maintain stable and scalable solutions to extract data from diverse systems, cleanse and transform extremely large data sets into actionable business information
  • Writing and tuning complex SQL queries in a highly dynamic environment
  • Utilize knowledge of ML techniques (esp. Neural networks and Genetic Algorithms) to ensure that models are updated that models being used for the recommendation engine are to some extent “explainable”
  • Use knowledge of therapeutic area, market events, customer universe and available activity and affinity data, along with guidance from the Advanced Analytics and Data Science team (AADS) to implement alternate advanced analytical and statistical techniques as and when needed to test and validate models embedded in the personalization platform

Skills And Expectations

  • 0-2 hands-on experience with handling data with strong coding experience in R or Python
  • Creative problem solving, organization, attention to detail, flexibility and adaptability
  • Willingness to learn and deploy ML models; agility in learning to communicate complex analytics concisely
  • Demonstrated ability to meet deadlines while managing multiple large-scale projects in a fast-paced and rapidly changing environment
  • Ability to execute and innovate in a highly dynamic environment, including strong prioritization and attention to detail
  • Excellent communication (written & verbal) skills

A Plus If You Have

  • Prior experience using ML techniques to deploy, assess performance and fine tune recommendation engine models (CNN/RNN, GA, XGBoost etc..)
  • Familiarity with cloud technology such as AWS and knowledge of AWS tools, esp Sagemaker, Quicksight, Redshift, Glue, Athena; viz tools like Tableau and PowerBI will be a plus

Education

  • Bachelor’s degree or master’s degree in technology, Statistics or Computer Science background
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