IBU Omnichannel Operations – Associate, AC, Sr AC
4 weeks ago
We’re looking for people who are determined to make life better for people around the world.
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 “next-best action recommendation engine” platform to enable more agile sales and marketing operations. This platform will
- ingest a wide variety of data sources,
- run advanced personalized marketing models
- and integrate seamlessly with all other Lilly SF and marketing 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.
As part of this team, we are excited to welcome professionals interested in driving cutting-edge technology-driven business operations, with a focus on quality assurance and continuous improvement. This team will be responsible for operating the omnichannel recommendation engine platform (ZAIDYN) which will drive SF and marketing campaigns for IBU Eli Lilly.
Core Responsibilities:
As part of this team, we are looking for experienced 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 IBU Eli Lilly.
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.Technical skills:
3+ years of in-depth hands-on experience with handling pharma data, including but not limited to, market basket and sales data, customer 360 profiling, channel activity data (sales rep calls, email campaigns, etc) Strong coding experience in R or Python Creative problem solving, organization, attention to detail, flexibility and adaptability. Comfort with ambiguity and a willingness to work with a) operational teams to ensure timely deployment and b) marketing and strategic teams to ensure simplified communication of “black box” ML models. Strong understanding, through extensive prior delivery and leadership, of how Big Data platforms and data services fit into AI-driven personalized/omnichannel marketing operations.Behavioral competencies
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 at the program level. Ability to lead small offshore teams with multiple stakeholders in a large cross-office matrixed team, including vendors. Excellent communication (written & verbal) skills. High level of motivation and a strong desire to find creative solutions to challenging situations, with the ability to avoid reinventing the wheel.Preferred Qualifications
Prior experience using ML techniques to deploy, assess performance and fine tune recommendation engine models (CNN/RNN, GA, XGBoost etc..), esp for pharma/biotech/insurance/other healthcare players. 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
Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.
#WeAreLilly
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