Data Scientist (R, Python, Machine Learning, Healthcare Domain)

Overview

On Site
Full Time

Skills

Pharmaceutics
Medical Records
Writing
Publishing
Presentations
Clinical Trials
Management
Technology Assessment
Quality Control
Electronic Commerce
Statistics
SAS/SQL
Macros
Research
Analytical Skill
Training
Unix
Apache Hadoop
Accountability
Collaboration
Teamwork
Conflict Resolution
Problem Solving
Analytics
R
Python
Data Science
Visualization
Machine Learning (ML)
Health Care
Database
Electronic Health Record (EHR)

Job Details

Job Description:
Role is responsible for designing, planning, and executing analytical components of plans for research studies that examine the value of pharmaceutical assets or address strategic questions in the disease areas of interest to Client. Position applies methodological and analytical expertise to conduct or consult on the analysis of RWE studies. This involves the application of statistical theories, 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; he/she 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. He/she is expected to standardize coding convention, processes and protocol translations to increase efficiency.
Responsibilities:
  • He/she is expected to have the potential to be the analytics lead for the assigned therapeutic area (TA) to mitigate, resolve and triage certain issues and reduce the touch point needed from the study lead.
  • Responsibility for analytical component of reports describing studies, outcomes and methods used and provide specifications to the V&E researchers.
  • Presenting results of studies and contribute to the writing and publishing of scientific presentations.
  • Identifying appropriate internal and external data resources and external expertise to execute strategies and research activities led by V&E members.
  • Supporting clinical trial endpoint strategies by executing statistical analysis plans and providing consultation to design as needed.
  • 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 overseeing problem solving within the analytical team.
  • Interactions with V&E TA scientists regarding studies and with other members of the Analytical team that perform QC activities on their studies.
  • Translating V&E business needs into RWE project concepts and collaborating with HEOR scientists to develop study protocols.
Requirements:
  • Master's degree in Statistics or related discipline required, PhD in statistics or closely related discipline preferred.
  • Minimum of 1-2 years' experience in SAS, SQL programming/R/Python required.
  • bility to incorporate production code macros in studies required, ability to execute programming assignment 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.
  • 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.).
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