Caring for the world, one person at a time inspires and unites the people of Johnson and Johnson (J&J). We embrace research and science – bringing innovative ideas, products and services to advance the health and well-being of people. Employees of the J&J Family of Companies work with partners in health care to touch the lives of over a billion people every single day, throughout the world.

The Medical Device Epidemiology – Real World Data Sciences organization at Johnson and Johnson (J&J) conducts observational research to support product development, product launch, post-market safety and effectiveness, value assessments, and business development of medical devices. The team leads Real World Data (RWD) innovation and methodological excellence across J&J’s medical device sector. The team generates real-world evidence to answer questions related to epidemiology of the indication, safety, product development, label extensions, value demonstration for payers, and other activities.

The Real World Data Analytics Lead will oversee the overall data programming and analytical needs of the Medical Device Epidemiology – Real World Data Sciences organization. The position will lead the development of standardized process that will maintain and improve the quality, consistency, and efficiency of the team’s analytical deliverables. Additional responsibilities include, but are not limited to the following:

Supervise senior analysts and off-shore programming team on their day to day programming activities

Build processes for onboarding, training and project prioritization for members of the analytics team

Lead the development and validation of standardized approaches to programming and analytical tools, including R, SAS, and SQL macros

Coordinate the creation of data specification files and Frequently Asked Questions (FAQ) documents for healthcare datasets such as electronic medical records, insurance claims, hospital billing data

Oversee the development of frameworks for commonly used statistical methods in observational research such as propensity score matching/stratification, survival analysis, and other regression models

Build infrastructure and processes for data storage, code repositories, documentation and quality control to ensure the highest quality of deliverables

Coordinate analytical forums and training sessions for dissemination of standardized processes, frameworks, macros and new analytical approaches

Collaborate with other functions to assess, evaluate and ETL new data sources

Manage analytical infrastructure issues related to software and data servers in close collaboration with colleagues from IT and Data Sciences

Implement new approaches for working with “big data” in healthcare. Some tasks may include data visualization, development of GUI/dashboards, exploring common data models, predictive analytics and machine learning algorithms

Ensure that the analytical platforms are operational for the team to meet their goals

Qualifications

An advanced degree (Masters or PhD) in Biostatistics, Public Health, Epidemiology, Informatics, Computer Science or another related field is required

At least 5 years of experience in database programming (e.g. SQL) is required

At least 5 years of experience in statistical programming (e.g. R, SAS) is required

At least 5 years of experience in programming against large healthcare datasets is required

In-depth knowledge of the structure and caveats of healthcare claims databases, electronic medical records and/or hospital billing data is required

Knowledge in epidemiologic and statistical concepts related to observational research is preferred

Prior people management experience is preferred

Relevant prior work experience in the healthcare industry within a pharmaceutical or device company is preferred