You will need to login before you can apply for a job.
Site Name: USA - Pennsylvania - Upper Providence, UK - Hertfordshire - Stevenage
Posted Date: Dec 10 2024
Why GSK?
We are a biopharma company focused on uniting science, technology, and talent to get ahead of disease together. By the end of the decade, we aim to positively impact the health of 2.5 billion people as a successful, growing company where people thrive.
Why Research Statistics?
We play a key role in GSK's discovery of new medicines, collaborating with outstanding scientists to advance innovative ideas in drug discovery. We design experiments, analyze data, and visualize results for projects that range from small studies with single endpoints to high-dimensional genomic data. We use classical statistical methods, Bayesian techniques and modern machine learning algorithms. Our work is varied and challenging with the common goal of advancing the science of human health.
Our team is part of GSK's Biostatistics function - a large group of statisticians, programmers, and data scientists with the mission to put statistical thinking at the heart of R&D decision-making. Our colleagues design and analyze clinical trials, but we focus on laboratory experiments and the re-analysis of randomized and observational data to identify targets, biomarkers, and patient sub-populations. To succeed, our team members need good technical and communication skills and the ability to learn rapidly, to develop practical solutions, and to apply statistical techniques in creative ways. Our staff enjoy solving challenging problems and developing the skills and knowledge together to improve the health of millions of people.
Key Responsibilities:
1. Work with scientists and leaders to identify and define scientific objectives that benefit from statistical approaches and propose implementation strategies.
2. Examine relevant literature and perform simulations to assess the applicability of statistical methods to challenges in drug discovery.
3. Craft experiments with clear objectives that address important scientific queries.
4. Analyze data using established statistical methods and develop novel methods to address important scientific questions.
5. Clearly communicate results to fellow researchers and managers at all levels.
6. Influence statistical strategy within and across projects and research initiatives.
7. Collaborate effectively with other quantitative groups (computational biology, AIML, etc.) to achieve research goals.
8. Propose and/or lead department initiatives to enhance skills or to establish department processes.
9. Represent Research Statistics on cross-department Biostatistics initiatives, influencing the direction and strategy of those initiatives and ensuring they are suitable for application to our team and its work.
10. Provide technical expertise to other statisticians within our team.
11. May include supervision and/or line management of other statisticians.
Why You?
Basic Qualifications & Skills:
We are looking for professionals with these required skills to achieve our goals:
1. PhD in statistics, biostatistics, or a closely related field with formal statistical training
2. At least 5 years (post PhD) of professional experience as a statistician
3. Minimum of 2 years experience in advanced statistical modelling and/or modern machine learning techniques
4. At least 6 years of experience writing code in R or SAS
Preferred Qualifications:
The following skills are not required, just preferred:
1. Ability to clearly communicate both new and established statistical methods to scientific colleagues in spoken and written English
2. Successful statistical consulting experience, working alongside researchers from other fields to devise practical solutions for scientific inquiries
3. Skilled in selecting, using, modifying, and developing statistical methods to address complex scientific questions effectively
4. Continuous learner, regularly expanding statistical and scientific knowledge
5. Broad experience using statistical methods with recognized and demonstrated expertise in at least one of these areas: experimental design, mixed models, Bayesian statistics, linear and nonlinear regression, repeated measures, machine learning algorithms, and high-dimensional data analyses
6. Knowledge of genetics, molecular biology, the process of drug discovery, and/or cellular and tissue-based model systems
7. Experience building and maintaining relationships with scientific collaborators
8. Experience setting statistical strategies for initiatives across multiple projects or departments and evidence of successful implementation
9. Experience identifying and applying emerging statistical methodologies to enhance study designs, analysis, and interpretation in scientific research
10. Demonstrated ability to take a leadership role in multi-disciplinary teams
11. Demonstrated ability to develop junior staff through mentoring and/or supervision
#J-18808-Ljbffr