We are currently recruiting for an Assessor, Methodology (Biostatistics).
This is a hybrid role with 2 days in the office, 3 days remote.
This is a 35 hour week.
ROLE SUMMARY
The position of Assessor, Methodology (Biostatistics) will be based in the Methodology team in the Centralised and Clinical Trials licensing section and will report to the Executive Assessor (Methodology).
The Methodology team delivers assessments for products authorised through the centralised (EU) mechanism, and clinical trials.
The Methodology team consists of clinical pharmacology assessors and biostatisticians/statistical assessors.
The Assessor, Methodology (Biostatistics) will use their statistical and professional expertise to lead the assessment and review of the statistical aspects of marketing authorisation applications and clinical trial applications.
The Assessor, Methodology (Biostatistics) will critically analyse complex clinical and scientific information, make sound judgements on the value of the statistical methodologies employed, discuss considerations with statistical and non-statistical colleagues and write informative assessment reports for a multi-disciplinary readership, contributing to benefit risk assessment.
The assessment may involve data analysis and interpretation, and dissemination of such to a multi-disciplinary audience.
The Assessor will provide expertise on statistical components of good drug development, interacting with colleagues, multi-disciplinary advisory committees and company representatives, as required.
The Assessor will contribute to providing in-house statistical expertise to other technical sections in HPAR, as required.
KEY RESPONSIBILITIES
Technical Objectives
Analyse and critically appraise statistical aspects of marketing authorisation applications, scientific advice and clinical trial applications and prepare assessment reports.
Assessment includes, but is not limited to, statistical methods, statistical design, statistical analyses plans, sample size and sensitivity analyses and imputation methods for missing data.
Strategic Objectives
Support the Section Manager, Executive Assessor (Methodology) and Section Leadership Team in the continued development of the section and the development, preparation and implementation of work objectives for the section.
Operational Objectives
Work with the Executive Assessor (Methodology) and Section Leadership Team to meet the goals and operational objectives of the section, and to organise work tasks to ensure efficient delivery of work.
Quality and Knowledge Management
Support the effective implementation of the Quality Management System.
Assist the managers of the section to ensure that procedures remain up to date with relevant developments in National, European and International regulations, legislation and guidelines.
Performance Management
Participate in the performance development programme (PDP) to support individual development.
Communication and Customer Service
Work with colleagues to maintain positive relationships with stakeholders that reflect the professionalism and high standards in the conduct of assessment and related activities.
Team Development
Support the Executive Assessor (Methodology) to ensure the provision of adequate technical, non-technical and continuous professional development for colleagues within the section and HPAR Department.
QUALIFICATIONS AND EXPERIENCE
To be considered for this post, candidates are required to meet the following criteria:
A relevant 3rd level honours degree (NFQ level 8) in mathematical, life science, or healthcare discipline (e.g. mathematics/applied mathematics, biostatistics, statistics, biomedical science, pharmacology, pharmacy, genomics, psychology).
A minimum of two years relevant experience in applying statistical methods in drug regulatory agency, biomedical research, pharmaceutical or CRO industry, or an additional post graduate qualification (NFQ level 10) in statistics, medical statistics or biostatistics.
Knowledge and understanding of the drug development process (pre-clinical, quality, clinical and post-approval).
Knowledge and understanding of the clinical trial process, clinical trial designs and various statistical approaches that may be used.
The ability to work as part of a cross-functional, multidisciplinary team and clearly communicate statistical issues and methods to multidisciplinary audiences.
Experience employing analytical thinking on complex data and statistics, and ability to make appropriate and tailored recommendations.
Sound understanding of statistical methodology relevant to the regulation of medicines and clinical trials, and emerging methodologies relevant to the field of statistics and medicines regulation.
Experience and knowledge with statistical software packages such as R and SAS.
Self-starter, accountable, capable of effective and consistent communication, negotiation and decision making.
Motivated to further develop professional skills and competencies, looks for opportunities to grow and take on new challenges.
Flexible to adapt to changing priorities and take responsibility to ensure success.
Good strategic ability (problem-solving, critical thinking, cross-functional thinking).
Strong organisational skills, including the ability to prioritise workload with autonomy.
Passion for knowledge sharing and dissemination of statistical expertise, and a willingness to promote continuous learning through peer-to-peer learning, discussion and reflection.
Proficient in the use of standard office software, such as Microsoft Office (Word, Excel, PowerPoint), cloud-based platforms (Microsoft Teams), and communication tools (Outlook, Teams).
In addition, the following would be considered an advantage:
A post graduate qualification (NFQ levels 9 or 10) in a relevant area such as statistics, medical statistics or biostatistics.
Awareness of centralised licensing and clinical trials regulatory processes in Europe.
Awareness of regulatory (ICH, EMA and FDA) guidelines relating to the regulatory requirements for medicines and clinical trials.
Knowledge and experience in one or more of the following areas:
Real-world data and Real-world evidence/pharmacoepidemiology.
Artificial intelligence/machine learning.
Bioinformatics, computational biology or statistical genetics.
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