Job:Biostatistician/Statistical Scientist in Oncology
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cgb@spbu ▴ 70

Biostatistician/Statistical Scientist in Oncology

Department of Oncology, Old Road Campus Research Building, Roosevelt Drive, Oxford

Grade 7: £30,434 - £37,394 p.a

Applications are invited for a Biostatistician post to join a rapidly developing Bioinformatics Research Core, based at the Department of Oncology, University of Oxford.

We have an exciting opportunity for a Biostatistician to contribute to and help lead on the analysis of large-scale data sets in molecular oncology. The purpose of the role is to provide Biostatistical expertise to the Core and contribute to the design of studies, the analysis of data and the development of new analytical methods. The person appointed will be interacting directly with researchers to help identify project funding opportunities, to maintain the scientific quality and integrity of projects and ensure their timely completion. You will focus on statistical analyses of cancer data sets assessing clinical/phenotypic data, genomics and other *omics biomarkers for association with the risk of disease and its progression.

RESPONSIBILITIES

  • To collaborate closely with biologists and clinicians to deliver biostatistics and bioinformatics analyses of datasets, to validate key findings and to suggest further experiments.
  • To develop innovative statistical solutions for integrated analysis of multidimensional *omics datasets, including gene expression, next-generation sequencing, high throughput screening (shRNA), metabolomics and proteomics data in the context of clinical studies and trials.

REQUIREMENTS

  • A PhD or MSc in Statistics, Biostatistics, Statistical Genetics, Applied Mathematics or similar subject. Advanced degree (PhD) is a strong plus.
  • A good understanding and ability to implement statistical techniques used in clinical studies/genomic research (e.g. logistic/linear regression, high-throughput data analysis with univariate and multivariate models taking into the account the complex correlation structure of the data, meta-analysis, survival analysis, statistical models for the analysis of longitudinal and event history data, etc.)
  • Experience in high-throughput data analysis, integration and interpretation (high-throughput sequencing, proteomics, array-based technologies, etc.), preferably in human disease and cancer.
  • Working knowledge of statistical software packages such as R/BioConductor, MATLAB and scripting skills in languages such as Python, Perl or Awk.
  • Independent ability to maintain state-of-the-art statistics/bioinformatics skills and implement and adapt new techniques for 'omics' research within the Department
  • An active interest in undertaking scientific research and the ability to learn new techniques and apply them to a high standard.
  • Extensive research experience and an ability to develop research projects
  • The ability to work closely with others as part of the Core, while taking personal responsibility for assigned tasks. Ability to collaborate effectively with other computational and wet-lab scientists.
  • Have a proven track record in the provision of technical advice and intellectual support with the ability to train others in tools relating for data processing and analysis
  • Enthusiasm for and a desire to make a career in bioinformatics and biostatistics.

The post is fixed-term for 2 years in the first instance and funded by the Department of Oncology. A lower grade offer may be made (Grade 6: £27,057 - £32,277 p.a.) with a commensurate reduction in responsibilities if a suitable candidate cannot be found to fill the Grade 7 position.

HOW TO APPLY

All applicants must complete a short application form and upload a CV and supporting statement. Please quote Vacancy Reference 117415 on all correspondence. https://www.recruit.ox.ac.uk/pls/hrisliverecruit/erq_jobspec_version_4.jobspec?p_id=117415 For informal enquiries, contact Anastasia Samsonova (bioinformatics@oncology.ox.ac.uk).

DEADLINE April 2, 2015

biostatistics cancer R genomics • 3.0k views
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