Statistical Consultant

JR United Kingdom; Southampton; Remote; GBP 70,000 - 100,000; Full time

Job summary

A leading company in the pharmaceutical sector is seeking a Statistical Science Associate Director to innovate statistical approaches. This remote role offers the opportunity to influence clinical studies across multiple therapeutic areas, contributing to drug development processes by providing expertise and leading project initiatives.

Qualifications

• Strong programming skills in R and/or SAS.
• Research record in statistical methodology, supported by publications.
• Research experience in safety data analysis and Bayesian methods.

Responsibilities

• Provide expertise in statistical methodology during drug development.
• Lead strategic projects to improve clinical trial design.
• Develop methodologies and engage with external collaborators.

Skills

Statistical solutions

Programming in R/SAS

Collaborative teamwork

Education

PhD in Statistics or related discipline

Job description

Role: Statistical Science Associate Director – Statistical Innovation

Location: Remote

Duration: 12 months

Description

Do you have a passion for developing and implementing innovative statistical approaches in clinical studies? Are you ready to impact a company that transforms ideas into life-changing medicines?

At AstraZeneca, we prioritize patients and strive to meet their unmet needs worldwide by applying cutting-edge science across various areas. Working here means being entrepreneurial, thinking big, and collaborating to turn the impossible into reality. If you are proactive, confident, collaborative, and curious about the potential of science, you are our ideal candidate.

We are seeking a contractor to join our growing Statistical Innovation group as a Statistical Science Associate Director. Our focus is providing statistical methodology support across all phases of clinical development for divisions including Cardiovascular, Renal & Metabolism, Respiratory & Immunology, Oncology, Rare Diseases, and Vaccines.

In this role, you will be part of the Respiratory Biometrics and Statistical Innovation department, leveraging data-driven approaches to efficiently and creatively bring the right medicines to the right patients. Our team utilizes expertise in statistics and programming to address drug development challenges and reduce uncertainties, facilitating better business decisions through quantitative reasoning.

Main duties and responsibilities

You will join a team of statistical methodology experts, providing key insights and solutions during critical stages of drug development. This technically challenging work requires delivering pragmatic solutions quickly, with refinement and development following initial implementation.

You will contribute to or lead capability building in areas such as:

• Design of early and late-phase clinical studies, including group sequential and adaptive designs using Bayesian and frequentist approaches.
• Analysis methods for real-time data, Estimands, Missing Data, Subgroups, and Biomarkers.

Additionally, you will provide expert consultancy to statisticians, medical scientists, and other stakeholders, leading strategic projects that improve clinical trial design and analysis. This includes developing new methodologies, engaging with external scientists and regulators through publications and collaborations, and identifying new areas where Statistical Innovation can add value.

Requirements:

• PhD in Statistics or a related discipline, with experience in academic research and/or clinical drug development.
• Proven ability to deliver innovative statistical solutions in applied settings, particularly in clinical trial design and data analysis.
• Strong programming skills in R and/or SAS.
• Experience in collaborative, cross-disciplinary teamwork.
• Research record in statistical methodology, supported by publications in reputable journals.
• Commitment to applying scientific expertise to benefit patients.
• Research experience in safety data analysis, observational studies, meta-analysis, Bayesian methods, and non-linear/mixed effect models.
• Awareness of evolving scientific and regulatory issues in statistics.