Data Analyst R&d Data Science & Digital Health
Johnson & Johnson, S.a.
Municipality of Madrid, Spain
6 days ago
Role details
Contract type
Temporary contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
EnglishJob location
Municipality of Madrid, Spain
Tech stack
Data analysis
Health Informatics
Computational Biology
Computer Programming
Data Mining
R
Python
Machine Learning
Information Technology
Job description
- Assist in building and maintenance of in-house and open-source tools, pipelines and resources.
- Collaborate in the design and implement data analysis studies/projects, including: data extraction and preprocessing tasks to prepare datasets for analysis, exploratory data analysis, innovative results/data visualization techniques, statistical and machine learning models.
- Communicate findings and insights through reports, presentations, and publications.
- Duration: 6 months
- Weekly hours: 39
Requirements
- Currently pursuing master's, or PhD degree in Epidemiology, Public Health, Biomedicine, Pharmacy, Statistics, Biostatistics, Data Science, Computer Science, Electrical Engineering, Computational Biology, Biomedical Informatics, or related quantitative field.
- Vocational training degree such as: "Técnico Superior en Administración de Sistemas Informáticos en Red", "Técnico Superior en Desarrollo de Aplicaciones Multiplataforma/Web"., * Demonstrated experience in analysis of large healthcare datasets/real-world data (EHR, insurance claims, registry data)
- User level skills on OMOP common data model and OMOP/OHDSI tools (Darwin, ATLAS, Cohort Diagnostics, Athena, etc.)
- Proficiency in R programming language. Strong programming skills in Python is a plus.
- Strong technical communication and presentation skills., + Designing/conducting observational studies
- Exploratory data analyses, statistical modeling, time-to-event analyses, comparative effectiveness analyses, causal inference methods to mitigate observed and residual confounding (e.g., propensity score matching/weighting, instrumental variables, state transition models)