At Johnson & Johnson, the business problem is to improve the design and execution of clinical trials through the application of cutting-edge data analytics and machine learning techniques.
Requirements
- Strong background in machine learning, natural language processing, and data modeling.
- Experience with large language models (LLMs), GPT, and RAG technologies and their applications in healthcare or clinical research.
- Experience in statistical modeling and analysis, including methods such as regression, time series analysis, or Bayesian modeling.
- Proficiency in programming languages such as Python and R, with experience in data manipulation and analysis libraries (e.g., Pandas, NumPy, scikit-learn).
- Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
- Experience with database management and querying languages (e.g., SQL).
- Understanding of version control systems (e.g., Git) for collaborative coding.
Responsibilities
- Apply LLMs and GPT technologies to streamline and enhance the extraction of key features from clinical trial protocols, including disease, drug, eligibility criteria, and endpoints.
- Utilize RAG techniques to improve the retrieval of relevant information from existing clinical trial data, facilitating optimized protocol design.
- Develop predictive models using advanced machine learning techniques to assess trial feasibility, timeline estimates, and operational efficiency based on protocol content.
- Design, build, and maintain efficient data pipelines that facilitate effective data utilization, as well as the creation, curation, and maintenance of key datasets.
- Collaborate with multidisciplinary teams to integrate data-driven insights into the protocol design process, ensuring that the complexities of clinical trials are adequately addressed.
- Design and implement innovative tools and frameworks for protocol enhancement, incorporating feedback from end-users to iteratively improve processes and outcomes.
Other
- Ph. D. in Data Analytics, Computational Sciences, Biomedical Informatics, or a related field.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work collaboratively in a multidisciplinary team environment.
- Familiarity with clinical trial design, phases, and regulatory requirements and clinical trial operational metrics.
- Understanding of statistical analysis in clinical trials, including concepts such as hypothesis testing, p-values, confidence intervals, and common statistical tests used in trial data analysis.
