RCI-ABBV-31471 Real World Evidence Analytics Scientist / Data Scientist (HEOR/RWE Analytics/SAS/SQL/Python/Machine Learning/Medical Claims Datasets Analysis)

Hybrid in Mettawa or Remote • Role is responsible for designing, planning, and executing large scale data analysis, and algorithm development to identify gaps in the care continuum and to measure impact using RWD. • This work highlights the value of utilizing real world data to inform or address strategic questions in the disease areas of interest to Client . • The role involves the application of statistical theories, analytical methods, and technical programming skills to analyze clinical, survey, claims, and electronic medical record • databases. Role supports the department as a technical subject matter expert on RWE analytics; candidate is expected to act as point of contact for the assigned therapeutic area (TA) on analytical needs, proactively mitigating, resolving and triaging issues and reducing the operational complexity needed. • candidate is expected to standardize coding convention, and processes to increase efficiency. Candidates must be adept at breaking down complex data into easily understood language and/or visualization for less technical audiences. Candidates should also be creative in the use of RWD and propose solutions to answer business questions. Responsibilities: • The candidate is expected to have the potential to be the analytics lead for the assigned therapeutic area (TA) to mitigate, resolve and triage analytical complexities • Presenting results of analysis and contribute presentation of findings and key takeaways for various audiences • Identifying appropriate internal and external data resources and external expertise to execute strategies and research activities • Executing analysis plans and providing consultation to design as needed • Work with Medical engagement to understand key impact variables and use RWD to quantify and operationalize these variables • Responsibility and accountability for meeting timelines in complex matrix structure. This will require developing and maintaining effective cross-functional working relationships to assure effective teamwork and overseeing problem solving within the analytical team • Interactions with Medical Engagement, Business Unit TAs, HEOR, and RWE CoE regarding findings and with other members of the Analytical team that perform QC activities on their studies • Translating TA business needs into project concepts to develop supporting data dictionary and corresponding analysis Qualifications: • Master’s degree in Statistics or related discipline required, PhD in statistics or closely related discipline (e.g., Health Economics and Outcomes research, Epidemiology, health services research etc.) preferred • Minimum of 1-2 years’ experience in analytics programing (SAS, SQL, R, Python) required. The candidates with Python and machine learning experience are preferred. • Ability to incorporate production code macros required, ability to execute programming assignments and problem solve issues independently, and ability to present complex research and data clearly to stakeholders. • Experience and/or training in the application of advanced scientific and analytical methods in real world evidence generation • Ability to perform urgent analyses that are needed with short turnaround time and interact with internal and external collaborative partners on joint projects • Professional training in a healthcare field, experience in analysis of large medical claims datasets or prior experience in the UNIX/Hadoop environment preferred What are the top 3-5 skills, experience or education required for this position: • Responsibility and accountability for meeting timelines in complex matrix structure. This will require developing and maintain effective cross-functional working relationships to assure effective teamwork and problem solving within the analytics team • Proficiency in R and/or Python • Data Science techniques (Visualization/ Machine learning skill sets) • Epidemiological design/methods • Familiarity with traditional healthcare databases (e.g., administrative claims data, electronic medical records, etc.)