Statistics Director, Research Statistics

Location: Stevenage, United Kingdom

Schedule: Hybrid – mix of 2 to 3 days onsite per week and part time remote work

Job group: Digital Data & Analytics

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 work spans multiple disease areas and drug discovery technologies.

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. We hire smart, creative people who enjoy solving challenging problems and developing the skills and knowledge together to improve the health of millions of people.

Key Responsibilities:

In this role, you will lead the delivery of statistical consulting for scientific research projects within GSK drug discovery.

1. Work with GSK scientists and leaders to define and answer pivotal drug discovery questions using statistical techniques.
2. Pinpoint major scientific inquiries that can benefit from statistical approaches and propose implementation strategies.
3. Develop and implement advanced statistical models for ‘omics data using statistical methods and machine learning techniques.
4. Manage a compact team (2-5 individuals) of MSc and PhD statisticians, providing technical and strategic guidance to enhance their work and develop their skills.
5. Craft experiments with clear objectives that comprehensively address scientific queries.
6. Present statistical principles, designs, and results transparently and precisely to GSK researchers and management across all levels, improving the understanding of quantitative results and contributing to better quantitative decision-making across drug discovery.
7. Lead department initiatives and cross-functional matrix collaborations to enhance specific technical skills and refine procedures within our team and across drug discovery.
8. Collaborate with other in-house quantitative groups (computational biology, AIML, etc.) to achieve shared goals.

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 and 10+ years of professional experience as a statistician.
2. Evidence of innovation and technical strength in advanced statistical modelling and/or modern machine learning techniques.
3. Experience writing code in R or SAS.

Preferred Qualifications:

The following skills are not necessary, just preferred, so you should still apply without them:

1. Proficient in both spoken and written English, with the capability to clearly communicate both new and established statistical methods to peers in science.
2. Background in statistical consulting, working alongside researchers from various fields to devise practical solutions for scientific inquiries.
3. Skilled in choosing and employing statistical techniques effectively to address complex scientific questions.
4. Experience in both recognizing and innovating statistical methodologies to enhance study designs, analysis, and interpretation in science research.
5. Assess, endorse, and put into practice statistical tools tailored for scientific issues.
6. Quick and independent acquisition of new statistical knowledge and skills.
7. Driven by a continuous aspiration to expand statistical knowledge and tackle demanding projects within pharmaceutical research.
8. Familiarity with and application of statistical approaches in areas including but not limited to experimental design, mixed models, Bayesian statistics, linear and nonlinear regression, repeated measures, machine learning algorithms, causal inference, and high-dimensional data analyses.
9. Knowledge of genetics, genomics, molecular biology, the process of drug discovery, and cellular, tissue-based, and animal model systems.

Demonstrated ability to:

1. Lead cross-functional teams.
2. Build and maintain relationships with scientific stakeholders.
3. Drive the adoption of statistical best practices within Research & Development.
4. Develop junior statisticians through mentoring and supervision.
5. Passion for continually learning and applying new statistical skills to solve challenging problems in drug discovery.

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