Real-World Evidence Informatics Scientist

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Lilly is a global healthcare leader who unites caring with discovery to make life better for people worldwide. We were founded more than a century ago by a man committed to creating high-quality medicines that met real needs, and today we remain true to that mission in all our work.

Overview:

The Real-World Evidence (RWE) Informatics role will be the technical leader for this group to advise the analysts and set direction on the use of reusable code. This will be the first RWE Informatics role supporting international work and providing technical leadership and coaching to others.

This role will also help address the integration of clinical concepts and RWE insights by leveraging analytic, clinical, scientific, and technological expertise. The scientist may have duties ranging from analysis of real-world data, advancing health information standards and use, and delivering innovative tools and improved practices to increase the efficiency of real-world evidence work. This role will be part of an analytic team that delivers on Lilly portfolio work for other business units. The analytic work for VEO International Business Unit will be done by this role and analysts. It will also do work across all areas of Lilly’s portfolio ensuring the role is focused on the highest priority work.

Responsibilities:

RWE Observational Research Analysis

• Provide scientific and analytic input to protocol, develop statistical analysis plan, and participate in drafting manuscripts and external disclosures.
• Transform real-world data (RWD) sources to analysis-ready data sets, enabling rapid evidence generation. In addition, perform quality checks on RWD to ensure fit-for-purpose.
• Apply the spectrum of advanced analytic methods to data based on an understanding of the healthcare system to deliver real-world evidence and interpret results to improve drug development teams. 

Health Information Standards and Delivery

• Provides leadership and expertise on vocabularies, terminologies, or coding systems structured to describe patients' care and treatment unambiguously. 
• Contributes to developing, communicating, and adhering to RWE health information and processes related to clinical standards, tools, and technology.
• Remains informed on the latest trends and standards in acquiring, representing, and delivering RWE health information.

Health Information System Solutions

• Proactively drive the use of data processes and tools to accelerate the development of the RWE STRADA Platform. Including innovative capabilities to improve the effectiveness of the platform.
• Continually seeking and implementing means of improving processes to reduce cycle time and work effort and to maximize the return on investment (i.e. technology purchases, data strategies, etc.).
• Define, implement and be accountable for a lifecycle management strategy for RWE health information, including that the structure, content and meaning, acquisition, storage, retrieval, interchange and representation of the RWE health information system are documented and well understood.
• Understand the confidential nature of RWD, personal health information, and company information and take necessary steps to protect it. 

Basic Requirements:

• Advanced degree in an analytics-related field (i.e. Informatics, Data Science, Epidemiology) or at least five (5) years of experience in real-world evidence, drug development or commercialization. 
• Proficient in the use of health information tools and analytics languages (i.e. R, Python, SAS, and SQL).
• Capable of implementing epidemiological analysis methodologies and using advanced analytic techniques such as machine learning, data/text mining, or visualization.
• Demonstrated experience in building analysis methods and systems for processing, organizing, analysing, and visualizing clinical data.
• Expertise in foundational data archetypes of structured/unstructured and other forms of real-world data.
• Excellent communication and comprehension skills with a proven ability to disseminate complex scientific information clearly and concisely.