Director, R&D Data Science & Digital Health, Immunology RWE - Barcelona
Role details
Job location
Tech stack
Job description
- Contribute to the design and execution of a Data Science & Digital Health (DSDH) RWE (Real World Evidence) strategy in alignment to the Immunology Therapeutic Area (IMM TA) goals and objectives
- Partner with DSDH colleagues and the IMM TA to define and prioritize a portfolio of innovative medicines in development in alignment with the IMM TA strategy
To successfully meet these objectives, this individual will work closely with individual clinical project teams as well as functional area partners in Discovery, Late Development, Epidemiology, Market Access, Medical Affairs and other relevant functions., * Provide strategic input into the Immunology R&D DSDH RWE priorities ranging from individual projects to large collaborations with internal functional areas and external institutions
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Identify viable data science and digital health opportunities and then conceive, develop and implement end to end study design and data analytical solutions. A specific example may include working with the Disease Area Stronghold (DAS) Leaders and Compound Development Teams (CDT) to develop the RWE DSDH strategy to improve early and late phase clinical study designs by supporting novel patient identification/ selection of end points (e.g., identification of at-risk patient populations through development of novel predictive algorithms)
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Serve as a key liaison to functional area partners and provide support for collaborations with DSDH, Epidemiology, Discovery and other internal functional areas to assure alignment on key DSDH programmatic goals aligned with the Immunology therapeutic area.
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Execute the DSDH RWE strategy across a matrixed cross-functional CDT and the Clinical Team.
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Provide strategic leadership on managing/addressing specific project/program-related issues and presenting to and negotiating with leadership teams on development plans or program-related issues.
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Participate in and may lead cross-functional teams for evaluation of new scientific opportunities, disease areas, product ideas, implement franchise business strategies, etc.
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Provide key input into diligence activities
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Participate or lead cross-departmental or cross-functional projects with broad Johnson and Johnson Innovative Medicine impact
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Author abstracts and scientific manuscripts for publication based on clinical trial data, real-world data, observational studies etc.
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Build credible relationships with opinion leaders and may act as company spokesperson regarding publication of research findings and presentations to relevant health authorities and consultant/advisory meetings.
This is not an exhaustive, comprehensive listing of job functions. May perform other duties as assigned.
Requirements
Do you have experience in SQL?, Do you have a Master's degree?, * PhD, PharmD, or MD (or equivalent) in statistics, bioinformatics, computational biology, epidemiology, applied mathematics, computer science, physics, engineering or related fields, is required. (Consideration may be given for candidates with a Masters degree and at least 10 years of experience.)
- 8+ years of progressive business experience in the pharmaceutical R&D, data science, digital health, including life sciences companies, consulting firms with established healthcare Data Science and life sciences practices, and other companies in the data science ecosystem
- Consistent track record of collaboration in a matrix organization, entrepreneurial skill, and ability to influence and engage strategic and technical partners
- Familiarity with data science and digital health Experience with healthcare datasets, such as EHR, or insurance claims
Preferred Skills:
- A strong background in RWE/Data Science & Digital Health
- Knowledge of Immunology is strongly preferred
- Experience delivering data science projects using predictive technologies, data mining and/or text mining
- Experience analyzing or handling healthcare data sets, including EHR, claims and registry data
- Experience with data science tools and statistical programming languages, including SQL, Python, R, and others
- Experience with defining use cases for deep learning, foundational models, and machine learning