Senior/Principal Data Scientist, Real World Data, Personalized Healthcare (PHC)

POSITION SUMMARY

As a Senior or Principal Data Scientist and RWD-RCT Hybrid Design Strategy Lead within our Personalized HealthCare function, you will work with 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. As an RWD-RCT Hybrid Design Strategy Lead within PHC Data Science RWD Collaborations, you will use your skills as an experienced drug developer and quantitative scientist to develop and execute on strategies to incorporate RWD into RCTs via hybrid designs and into single arm trials via external controls. You will be expected to design and implement strategies reaching beyond the function, and shape partnerships through widely recognized thought leadership. You will lead teams in matrix structures and maintain a wide network (internally and externally) of influential stakeholders. You will apply your deep and broad expertise across a large variety and multitude of challenges to deliver impact to the business. This position requires extensive cross functional collaborations working with a diverse team of clinical subject matter experts, data scientists, statisticians, and business leads. You will also contribute to functional, cross-functional, enterprise-wide or external initiatives that shape our business and healthcare environments. 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 an entrepreneurial mindset, to transform the way we use data and analytics to develop and deliver medicines for our patients.

Responsibilities:

• LEAD RWD-RCT HYBRID DESIGN STRATEGY: Develop and execute strategies in partnership with molecule teams to incorporate RWD into RCTs using hybrid-design approaches.
• 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.
• 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.
• 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 EXPERT 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 oversee the study, including its reporting; ensure compliance with applicable pharma industry regulations and standards.
• INTERPRET AND SHARE RESULTS: Communicate findings to internal stakeholders, 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).
• COLLABORATE & SHAPE: Collaborate and contribute to functional, cross-functional, enterprise-wide or external data science communities, networks, collaborative groups, initiatives on knowledge-sharing, methodologies, innovations, technology, IT infrastructure, policy-shaping, processes, etc. to enable broader and more effective use of data and analytics to advance science. 

Minimum Requirements:

• 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)
• 5-10+ years of experience in pharmaceutical industry
• Pproven ability to translate and communicate complex study design and findings to diverse audiences
• Deep subject matter expertise with proven ability to transfer this expertise across the business; proven track record of setting new standards, advancing the field of expertise (internally and externally) and engaging & influencing executive leaders internally and externally (e.g., in academic setting).
• Demonstrated track record of developing and execution of data science research projects, patient-level data analyses (e.g., real world data, surveys, clinical trials, registries, claims, genomic or imaging data) with publications and presentations
• 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

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