Data Scientist III

Company : Katalyst HealthCares & Life Sciences

Location : New York, NY, 10055

Posted Date : 28 October 2025

Job Type : Other

Category : Mathematics

Occupation : Data Scientist

Job Details

Job Description:
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 analyse 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.

Responsibilities:

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.
bility 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.
bility 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.

Top Skills:

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.).