What are the responsibilities and job description for the Biostatistician Internship position at Baynovation, LLC?
We are seeking a highly motivated and detail-oriented Biostatistician Intern to join our dynamic team. The successful candidate will be responsible for supporting our data analysis efforts in the field of biostatistics. This internship will provide an opportunity for the candidate to gain hands-on experience in a real-world data analysis environment and develop skills in Python and R programming.
- Currently enrolled in a program in Biostatistics, Statistics, or a related field.
- Strong knowledge of statistical analysis methods and tools.
- Proficient in Python and R programming languages.
- Familiarity with data visualization tools such as ggplot2 or matplotlib.
- Excellent written and verbal communication skills.
- Ability to work in a team environment.
- Currently enrolled in a program in Biostatistics, Statistics, or a related field.
- Strong knowledge of statistical analysis methods and tools.
- Proficient in Python and R programming languages.
- Familiarity with data visualization tools such as ggplot2 or matplotlib.
- Excellent written and verbal communication skills.
- Ability to work in a team environment.
- Support data analysis for research studies and drug development using SAS, Python and R programming languages.
- Contribute to the development of medical code auto-identification tools.
- Work closely with external clients (pharmaceutical companies, CROs, and medical device firms) to understand business and regulatory needs and translate them into data-driven and AI-enabled solutions.
- Support clients in developing AI tools for processing, standardizing, and analyzing clinical and biomedical data.
- Handle large-scale medical and healthcare datasets, including clinical trial data, EHR-derived data, and real-world evidence.
- Design and implement data extraction workflows from heterogeneous sources (CSV, Excel, databases, APIs, and vendor deliverables).
- Build and optimize ETL pipelines to transform raw healthcare data into analysis-ready formats.
- Automate repetitive data processing and reporting tasks to improve efficiency, consistency, and scalability.
- Collaborate with other team members to ensure the accuracy and validity of the analysis.