Data Science Engineer

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 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.

Lilly’s Purpose:
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.

Connecting Patients and Technology:
Digital Health (DH) R&D Data Science Engineer role offers the unique opportunity to connect a passion for patients, clinical development and technology by leading the data engineering, data analysis, and AI/algorithm development activities in clinical studies to passively and objectively measure disease-relevant physiology in support LY drug development and Lilly’s digital health products, as well as driving new software products and AI-powered features for Lilly’s Sensor Cloud (i.e., Magnol.Ai platform). In this role, you will also be engaged with developing and validating digital biomarkers and ultimately, digital measures which can have significant regulatory, patient and clinical trial impact.

Purpose of the Role:
The purpose of the DH R&D Data Science Engineer role is to be responsible for trial level digital device data strategy including acquisition, storage, retrieval, interchange, delivery and representation. This requires an in depth understanding of wearable sensor data collection, data flow, data quality, data technology, dataset delivery, archiving and data standards. This role applies advanced data analysis tools and techniques to provide insights and actionable recommendations for the trial, and utilizes algorithms to derive clinically meaningful digital measures, and explain findings to business audience. This role also develops new software and AI products to evolve Lilly’s sensor cloud (Magnol.Ai) to the next generation AI-powered data platform.

The DH R&D Data Science Engineer role closely partners with the broader DH R&D team (most notably Sensor Lab, and Clinical Operations), cross-functional scientists and colleagues, stakeholders and third-party providers to define, implement, and deliver digital measure outcomes primarily for non-LY exploratory DH R&D trials via a key role in the Digital Health Innovation Hub being established in Singapore at the Lilly Centre for Clinical Pharmacology.

Primary Responsibilities:

- Design and Implementation: Lead the design, construction, and optimization of systems for data collection, data transformation, and quality verification for Lilly’s proprietary data sets and metadata
- Collaboration and Interpretation: Work closely with our internal and external SME to troubleshoot issues, as well as with our data scientists and analysts to efficiently utilize our curated data, and translate complex datasets into actionable insights
- Innovation with AI and LLM: Design, develop, and test new software features for next-gen sensor cloud (Lilly’s Magnol.Ai platform), this includes new software products for the platform and the integration of LLM to enhance Magnol.Ai data analysis capability for personalized medicine
- Continuous Improvement: Proactively identify and implement enhancements in data processing efficiency, integrity, and quality with a sharp eye towards elevating the standards of our data handling practices

Minimum Qualification Requirements:

- Master’s Degree in Computer Science, Analytics, AI, or Informatics, and at least five years of industry experience with data analysis and/or algorithm development.
- Strong programming skills, and knowledge of data structures and algorithms - SQL, Python/R, machine learning, deep learning.
- Excellent software development experience and problem solving skillsets
- Working knowledge of cloud architecture fundamentals and web security protocols

Other Information/Additional Preferences:

- Demonstrated ability to set and implement plans to development sensor based digital health technology
- Demonstrated ability to effectively partner/influence a virtual team and drive a technical project to deliver results
- Understanding and experience in Time Series Analysis, Generative AI, prompt engineering, and Large Language Model (LLM).
- A growth mindset