Senior / Principal Data Scientist, PHC Analytics

11 Sep 2019
11 Oct 2019
R & D , Bioinformatics
Full Time
Contract Type
Experience Level
Experienced (non-manager)
As a Senior Data Scientist or Principal Data Scientist within our Personalized Health Care function you will work with partners throughout the global organisation to use meaningful data 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 would have the primary responsibility to design and implement analyses using a variety of data sources such as electronic medical records, insurance claims, patient registries, clinical trials, genomics, imaging and patient reported data (surveys, digital etc.). 

You will collaborate with peers within the global product development function and other teams located around the world to support evidence generation strategies, identify evidence gaps and perform evaluation on available data sources. The evidence and insights generated will be utilized to inform the research and development of our molecules, and support healthcare decisions by patients, physicians, health authorities, payers, and policy-makers. This will require a deep understanding of molecule and disease area strategies, healthcare environments, as well as strong scientific and technical data science expertise. You will need strong strategic, collaboration and communication skills, as well as a forward-thinking mindset, to transform the way we use data and analytics to develop and deliver medicines for our patients.

As a Senior Data Scientist or Principal Data Scientist you will be responsible for a molecule/indication in Neuroscience and partner with cross-functional teams and external organisations with considerable independence.

Responsibilities :
    • Identify evidence needs & recommend data 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.
    • 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.
    • Be an authority in applying methods: Stay current with and adopt emergent analytical methodologies, tools and applications to ensure fit-for-purpose and impactful approaches.
    • Produce high quality analyses: Apply rigor in study design and analytical methods; plan for data processing; design a fit-for-purpose analysis plan, assess effective ways of presenting and delivering the results to maximize impact and interpretability; implement and/or handle the study, including its reporting; ensure compliance with applicable pharma industry regulations and standards.
    • Interpret and share results: Communicate findings to internal partners, regulatory, health technology assessment (HTA) bodies and scientific communities; publish results; participate in external meetings and forums to present your insights (e.g. congress/conference).
    • Develop data strategy & gain access to data: Develop strategic plans to access fit-for-purpose data sources to support evidence generation, and gain access to data through collaboration or data generation.
    • Drive implementation of data management solutions: Work closely with data partners to design and implement efficient data management tools and practice allowing access to high quality patient level data
    • Collaborate & craft: Collaborate and contribute to functional, cross-functional, enterprise-wide or external data science communities, networks, collaboratives, initiatives or goals on knowledge-sharing, methodologies, innovations, technology, IT infrastructure, policy-shaping, processes, etc. to enable broader and more effective use of data and analytics to support business.

Minimum Qualifications:
    • MSc, PhD or similar qualification in a quantitative data science discipline (e.g., statistics/ biostatistics, epidemiology, neuroscience) 
    • Proven experience in applying machine learning and artificial intelligence in medical domain  
    • Consistent track record of developing and execution of data science research projects, patient-level data analyses (e.g., real world data, insurance claims, clinical trials, registries, surveys and digital health) with publications and presentations
    • Demonstrated experience with leading 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 ambitious mindset and self-direction, ability to teach others and willingness to learn new techniques
    • Proficiency in English, both written and verbal
  • Preferred/Additional Qualifications:
    • PhD degree in a quantitative discipline as listed in Minimum Qualifications
    • Background in applying statistical and machine learning methods in Neuroscience in the clinical setup
    • Fluency in statistical programming languages (R,Python, etc.)
    • Experience implementing advanced analytics approaches (machine learning, longitudinal data analysis, etc.)
    • Experience producing interactive outputs (Shiny, etc.)
    • Contributor to open source packages, libraries or functions
    • Experience implementing reproducible research practices like version control (e.g., using Git) and literate programming

Roche is an equal opportunity employer.
Research & Development, Research & Development > Biometrics

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