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Job Summary

We are seeking an intern with a strong scientific mindset, natural curiosity, and a passion for learning to join our Data Science team as a Digital Health & Real World Evidence Data Analysis Intern .

This internship will focus on the statistical analysis and interpretation of data generated through digital health technologies and real world data sources. The intern will work at the interface of digital health, clinical research, and RWE analytics, contributing to the generation of meaningful insights from complex datasets such as wearable derived signals, mobile health data, EHR, claims, and registries.

The intern will work at the interface of digital health, clinical research, and RWE analytics, contributing to the generation of meaningful insights from complex datasets such as wearablederived signals, mobile health data, EHR, claims, and registries.

This is an excellent opportunity for someone interested in digital health analytics, biostatistics, or RWE to gain practical experience in a dynamic and innovative environment.

Task and responsibilities

• Conduct exploratory and statistical analyses of digital health datasets, including physiological signals, continuous monitoring data, and sensor-derived metrics.

• Apply statistical models (e.g., mixed effects models, longitudinal analyses, timeseries methods) to evaluate digital endpoints and health outcomes.effects models, longitudinal analyses, timeseries methods) to evaluate digital endpoints and health outcomes.

• Support algorithm development for data processing, feature extraction, and digital biomarker assessment.

• Assist in the design and execution of RWE studies using data sources such as EHR, claims, and disease registries.

• Clean, curate, and harmonize clinical, RWE, and digital health datasets to prepare them for statistical analysis.

• Perform quality control checks on raw and processed data.
  

Education : University degree complemented with a PhD or MSc in a relevant field, such as life sciences, data science, biomedical engineering, computer science, statistics or a related discipline.

Qualifications

• Strong interest in statistics, digital health analytics, clinical research, and real world evidence generation. world evidence generation.

• Proficiency in Python and/or R, including packages for statistical modelling, data manipulation, and visualization.

• Understanding of timeseries analysis, signal processing, or digital biomarker development is an advantage.

• Familiarity with clinical trial protocols, RWE data sources, and health data standards is a plus

• Excellent organizational skills with the ability to manage multiple tasks and priorities.

• Strong analytical and problem solving abilities with high attention to detail.solving abilities with high attention to detail.
  

The duration of the paid internship will be 6 months, with the possibility of extension to 12 months

The intern will have the opportunity to gain hands-on experience in the pharmaceutical sector, specially into the digital healthcare field and contribute to cutting-edge research at the intersection of clinical research and digital health.