Scientific Leader, Biomarker Statistics US or UK - GlaxoSmithKline plc (Darby)

  • Darby, PA


: $78,700.00 - $122,400.00 /year *

Employment Type

: Full-Time


: Scientific Research

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Within GSK, Clinical Statistics is part of the same group which includes clinical pharmacology modelling and simulation, epidemiology and statistical innovation. Our statisticians take part in world-class methodological research, as well as to fulfil a vital role on our multidisciplinary drug development teams. Our end-to-end project support model ensures that our statisticians collaborate across the entire range of drug development, from early clinical development, to registration and marketed product support.


The successful candidate will serve as an expert statistical leader in the Oncology Biomarker Statistics team and will have the opportunity to be closely engaged and significantly influence the translational medicine efforts and companion diagnostic development.

Key Responsibilities include, but are not limited to:

  • Provide high quality and timely support for the various biomarker projects across all phases of drug development pipeline in collaboration with other functional area scientists.
  • Embrace and consider a wide variety of classical and modern data analytic methods and solutions. Propose and implement optimal and fit-for-purpose statistical and computational solutions for various biomarker studies.
  • Proactively give and receive feedback on the projects supported by all members of the Biomarker Statistics team to ensure high quality statistical contributions for biomarker needs in the drug development pipeline.
  • Develop and automate statistical analysis and visualization tools for scientists and other statisticians.
  • Stay current with ongoing external research and literature on statistical methods and biomarker applications, maintain proficiency in applying a variety of classical and modern statistical methods and machine learning algorithms, and be competent in explaining and justifying the methods used.
  • Develop new statistical methods and machine learning algorithms for research topics of greatest need and stay active in the external scientific community via conference presentations, publications and collaborations.

  • The ideal candidate will possess:

  • Experience with a wide variety of biomarker data such as genomics, proteomics and imaging from various traditional and cutting-edge analytical platforms and demonstrated expertise in applying sound computational and statistical approaches for processing and analyzing data from these platforms.
  • Keen interest in learning the scientific and technological elements of the projects.
  • Demonstrated ability to proactively contribute to the project strategy and decisions beyond the core statistical elements.
  • An analytical and inquisitive mind with a proven track record for problem solving.
  • Well-honed written and oral communication skills are essential.
  • A proven ability to build effective and trusting relationships with members of interdisciplinary matrix teams.
  • A proven ability to build powerful networks within and outside the company and use these relationships to achieve support for the planning and implementation of innovative approaches.
  • Requirements:

  • PhD in statistics or related field with at least 5+ years of relevant pharmaceutical research experience with high-dimensional biomarker data from all phases of clinical trials, or Masters in statistics or related field with at least 10 years of relevant experience.
  • Strong statistical foundation with a wide breadth expertise in classical and modern statistical and machine learning methods.
  • Demonstrated ability to apply innovative and fit-for-purpose statistical methods to the analysis of biomarker and high dimensional data from clinical trials.
  • Strong programming skills in R, Python and Perl, with demonstrated ability to develop and automate analysis scripts and tools for broader use.
  • Excellent interpersonal and communication skills, with a proven track record of building and maintaining strong working relationships.
  • A demonstrated ability to explain novel and standard methods to scientific and clinical colleagues.
  • Preferred:

  • Background in biological sciences, bioinformatics and computational biology.
  • Strong track record of methodological and applied research, conference presentations and publications.
  • Experience in applying Bayesian statistical methods in biomarker applications.
  • Prior experience in oncology research projects and drug development.
  • Programming experience in other languages such as Python, Perl, SAS, etc.
  • Good understanding of drug development processes and strategies, with proven ability to apply innovative statistical thinking to meet project and business objectives.
  • Experience providing statistical support for the development of companion diagnostics
  • Why GSK? :

    At GSK, our mission is to improve the quality of human life by enabling people to do more, feel better and live longer. Our three world-leading businesses research and deliver innovative medicines, vaccines and consumer healthcare products. We need a talented and motivated workforce to deliver against our strategy. To achieve this, we strive to attract the best people and to create an environment that empowers and inspires.

    If you require an accommodation or other assistance to apply for a job at GSK, please contact the GSK HR Service Centre at 1-877-###-#### (US Toll Free) or +1 801 ### #### (outside US).

    GSK is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive equal consideration for employment without regard to race, color, national origin, religion, sex, pregnancy, marital status, sexual orientation, gender identity/expression, age, disability, genetic information, military service, covered/protected veteran status or any other federal, state or local protected class.

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    Associated topics: biostatistics, data analyst, probability, statistics, c

    * The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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