As an Associate Data Scientist/Data Scientist within our Personalized HealthCare (PHC) Analytics function, you will work with meaningful Real World Data (RWD) to generate impactful evidence and insights on our molecules/medicines and patients, that support R&D, advance scientific and medical knowledge, and enable personalized patient care and access.
You will collaborate with peers within the function and across the organization to develop evidence generation strategies, identify real world healthcare data sources (electronic medical records, insurance claims, patient registries, biobanks, or survey data) and design and execute analyses to address molecule and disease area questions. You need to have a good understanding of RWD, observational research and advanced statistical methodologies as well as strong hands-on analytical expertise and scientific understanding.
You will also need strong collaboration and communication skills, as well as an entrepreneurial mind-set, to transform RWD into valuable clinical insights to inform decision making and help support our patients.
- Be an expert in methodology: Stay current with and adopt advanced analytical methodologies, tools and applications to ensure fit-for-purpose and impactful approaches. Understand the underlying principles of the data science and statistical methodologies and ensure applied appropriately.
- Produce high quality and rigor analyses: Lead, develop, and execute high quality RWD analytics solutions using appropriate methodologies, tools, and best practices and ensure compliance with applicable pharma industry regulations and standards. Communicate findings to internal stakeholders.
- Interpret and share results: Communicate findings to internal stakeholders, regulatory, health technology assessment (HTA) bodies and scientific communities, publish results, or presenting your insights in the meeting or forum.
- Dive into Data: Develop a comprehensive and deep understanding of the data we work with and foster learning with colleagues using analytical tools and applications to broaden data accessibility and advance our proficiency/efficiency in understanding and using the data appropriately
- Identify evidence needs & solutions: Ask the right scientific questions, understand the evidence needs for research and development, regulatory and market access, and ideate and make recommendations on fit-for-purpose data and analytics solutions.
- Collaborate & Shape: Collaborate and contribute to functional, cross-functional, enterprise-wide or external data science communities, collaboratives, initiatives or goals on knowledge-sharing, methodologies, innovations, technology, IT infrastructure, processes, etc. to enable broader and more effective use of data and analytics to support business.
Who You Are
- MSc, PhD or similar qualification in a quantitative data science discipline (e.g., statistics/ biostatistics, epidemiology, bioinformatics, health economics, computational biology, computer science, mathematics, outcomes research, public health, biology, medicine, psychology)
- Experience using observational study/non-interventional study design to analyze patient-level data (electronic medical records, insurance claims, disease registries, or survey, clinical trials etc.)
- Fluency in statistical programming languages (R, Python, SAS etc.), experience producing interactive outputs (Shiny, RMarkdown, etc.), visualization tools, and contributor to open source packages, libraries or functions
- Experience with technologies required to undertake analyses on large data sources or with computationally intensive steps (SQL, parallelization, Hadoop, Spark, etc.)
- Demonstrated experience with implementing a range of statistical modelling techniques (multivariate modelling, longitudinal data analysis, time-to-event analysis etc.) as well as an understanding of advanced analytics
- Experience implementing reproducible research practices like version control (e.g., using Git) and literate programming
- Demonstrated experience with managing project scope and driving delivery in an evolving environment requiring proactivity and effective problem-solving and prioritization when faced with challenges
- Demonstrated strong collaboration skills and excellent communication skills
- Demonstrated entrepreneurial mindset and self-direction, ability to teach others and willingness to learn new techniques
- Proficiency in English, both written and verbal
- PhD degree in a quantitative discipline as listed in Minimum Qualifications. Alternatively, MSc with proven work experience.
- Experience implementing advanced analytics approaches (machine learning, causal inferences, longitudinal data analysis, etc.)
- Contributor to open source packages, libraries or functions
Roche is an Equal Opportunity Employer & prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity/expression, national origin/ancestry, age, disability, marital & veteran status.