Roche is seeking an intern either pursuing, or recently graduated from a MSc/PhD degree with expertise in advanced analytics and machine learning to help us explore its potential applications in healthcare. You will join the Personalized Healthcare team and work on an innovative project applying advanced analytics and machine learning approaches to the company's real world data, genomics, and clinical trial data assets spanning multiple disease areas.
Of particular interest will be the application of graph-based machine learning techniques.
We are looking for individuals who are:
Creative problem solvers, quick learners and comfortable experimenting with new approaches
Demonstrate high productivity and enjoys dealing with ambiguity and applying novel methodologies
Possess entrepreneurship, passion and curiosity for understanding and interrogating complex data.
Responsibilities:
Collaborate with the host team and other stakeholders to evaluate potential machine learning techniques and applications, especially in the field of biomedical knowledge graphs
Design, build and interpret machine learning algorithms to address selected research questions (including preparing the input data)
Proactively share learnings and knowledge to support the development of the wider Roche Advanced Analytics community
Help shape the direction of machine learning and artificial intelligence within Roche
Experience and Competencies Preferred:
Knowledge of a wide range of machine learning techniques and applications, with a focus on knowledge graph modeling and prediction
Experience applying machine learning algorithms and techniques, preferably to genomics and healthcare (EHR) data
Experience with mechanistic graph learning methods as well as representation learning and graph embeddings. Previous work with biological networks and knowledge graphs can be of advantage.
Experience with advanced missing data imputation methods and state-of-the-art mechanistic graph learning methods as well as representation learning and graph embeddings. Experience with biological networks and knowledge graphs can be of advantage.
Experience with technologies required to undertake analyses on large data sources or with computationally intensive steps (SQL, parallelization, Hadoop, Spark, HPC cluster computing, Docker)
Fluency in statistical programming languages (R, Python, etc.)
Strong communication and collaboration skills
Experience implementing reproducible research practices like version control (e.g. using Git) and literate programming
Demonstrated contributions to open source packages, libraries or functions
Qualifications Required:
MSc/PhD degree candidate or recent graduate in Data Science related field (e.g., Statistics, Mathematics, Epidemiology, Health Economics, Outcomes Research, Computer Science
Job Level:
Entry Level
