Roche's Innovation Accelerator (IA) team is looking to solve the business and technical problem of transforming medicine development through AI/ML-powered digital solutions. They aim to unlock actionable insights from complex data to create scalable, ethical, and impactful intelligent tools within regulated healthcare environments.
Requirements
- You are working proficient with Python or R, and have basic familiarity with ML libraries (e.g., scikit-learn, XGBoost).
- You have an understanding of core statistical concepts, supervised learning, and experimental design.
- You have exposure to basic software development practices including version control and testing.
- You are familiar with one or more of the following areas: observational data (e.g., EHR), Bayesian methods, biomarker analysis, or knowledge-based ML.
- Familiarity with ML development tools or platforms (e.g., Docker, MLflow, Airflow).
- Experience working with structured or semi-structured healthcare datasets.
- Awareness of regulatory or compliance frameworks such as GxP or HIPAA.
Responsibilities
- You support the development of statistical or ML models to address defined scientific and business questions.
- You conduct exploratory data analyses and feature engineering on clinical and operational datasets under supervision.
- You assist with simulation studies and benchmarking of analytical approaches.
- You contribute to the implementation of reproducible pipelines for preprocessing and model development using standard tooling.
- You work with engineering and science teams to support the deployment of simple models into user-facing applications.
- You monitor basic performance metrics and assist in documenting model specifications and limitations.
- You apply basic principles of responsible ML such as traceability and explainability in collaboration with more senior colleagues.
Other
- You have a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Applied Mathematics, Bioinformatics, or a related field.
- You have a minimum of 2–4 years of hands-on experience applying data science or ML techniques to real-world problems in academic, internship, or industry settings; or an advanced degree with 0–2 years of equivalent work experience.
- You have attention to detail and quality work with an ability to manage and prioritize multiple projects simultaneously, including both long-term and short-term initiatives.
- You have excellent collaboration skills, including statistical consulting skills, interpersonal skills to contribute effectively in cross-functional team settings, ability to influence others without authority, and ability to build strong collaborative relationships with scientific and non-scientific partners.
- You exhibit excellent strategic agility including problem-solving and critical thinking skills, and agility that extends beyond technical domain.
