Applications for this job are now closed
Roche is seeking an intern pursuing a PhD degree with expertise in advanced analytics and machine learning to help us build predictive models of adverse drug reactions (ADRs) using Real World Data (RWD). Cardiac-related adverse drug reactions are among the most common reason for discontinuation or restricted use of drugs, and whilst the pharmacological targets of many drugs may contribute to this risk, the ADR potential of the drugs themselves can vary significantly across patients depending on the individual, the disease being treated, past and current comorbidities, and exposure to other drugs. As such, understanding and characterising the complex interactions between all of these features is an essential component of the safe and effective use of these medicines.
What you'll do
The intern will join a cross-functional team of scientists and data scientists and work to develop a machine-learning model to predict drug-related cardiotoxic events, making use of the company’s extensive real world data assets (e.g. electronic medical records, insurance claims, medical transcripts), genomic, diagnostic, imaging and clinical trial data assets spanning multiple disease areas. We are looking to combine these data-driven approaches with a more mechanistic approach describing the pharmacological activity of the drugs in question to allow us to generalise beyond drug/dose/route-of-administration specific models.
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
- Design, build, and interpret statistical or machine-learning models to predict individualised patient ADR risk
- Explore the use of different Roche RWD assets, including but not limited to claims databases and electronic health records
- Explore the integration clinical and mechanistic features (e.g. PK PD properties) in a single model
- Proactively share learnings and knowledge to support the development of the wider Roche Advanced Analytics community
Experience and competencies preferred
- Knowledge of a wide range of machine learning techniques and applications
- Experience applying machine learning algorithms and techniques, preferably to healthcare data
- 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
Qualifications required
PhD degree candidate in Data Science related field (e.g., Statistics, Mathematics, Epidemiology, Health Economics, Outcomes Research, Computer Science)
Application process
Stage 1: CV and cover letter, video interview
Please ensure you include a CV and a cover letter with your application. As part of your cover letter, Roche is keen to understand your motivation in applying for this position. Your cover letter should not exceed one page. Please note that all applications without a cover letter will be rejected automatically.
Video Interview: Once the HR review is complete, you will be invited to complete a video Interview to support your application.
Stage 2: Technical Assessment and interview
If you are successful at the CV, Cover Letter and Video screening, you will be invited to the final stage of the application process. This will include a face-to-face/online interviews, and relevant activities critical for the role, giving you a taste of Roche and the opportunity.
