Statistical Consultant

JR United Kingdom; Bournemouth; Remote; GBP 80,000 - 120,000; Full time

Job summary

A leading company in the pharmaceutical industry is seeking a Statistical Science Associate Director for their remote Statistical Innovation group. This role focuses on innovative statistical methodologies for clinical studies. The ideal candidate will have a PhD, extensive experience in drug development, and strong programming skills in R or SAS.

Qualifications

• Experience in academic research and clinical drug development.
• Research record with publications in reputable statistical journals.
• Desire to apply scientific expertise to patient-benefiting problems.

Responsibilities

• Provide key input to solve problems at critical drug development stages.
• Design early and late-phase clinical studies using innovative methodologies.
• Lead strategic activities to improve clinical trial design and analysis.

Skills

Statistical Solution Delivery

Collaborative Skills

Programming in R and/or SAS

Education

PhD in Statistics or related field

Job description

Role: Statistical Science Associate Director – Statistical Innovation

Location: Remote

Duration: 12 months

Description:

Are you passionate about developing and implementing innovative statistical approaches in clinical studies? Do you want to impact a company that transforms ideas into life-changing medicines?

At AstraZeneca, we prioritize patients and strive to meet their unmet needs worldwide. We apply advanced scientific approaches across various business areas. If you are proactive, confident, collaborative, and curious about science, you might be a great fit.

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 clinical development phases for AZ’s Cardiovascular, Renal & Metabolism, Respiratory & Immunology divisions, and targeted support for 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 medicines to patients. Our team uses expertise in statistics and programming to address drug development challenges and reduce uncertainty, enabling better business decisions.

Main duties and responsibilities:

You will join a team of statistical methodology experts, providing key input to solve problems at critical drug development stages. This involves producing pragmatic solutions within tight timelines, focusing on delivering first, then refining your work.

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 both frequentist and Bayesian methods.
• Analysis of real-time collected data, Estimands, Missing Data, Subgroups, and Biomarkers.

You will also provide expert consultancy to statisticians, medical scientists, and other stakeholders, leading strategic activities like capability build projects that improve clinical trial design and analysis. Additionally, you will develop and implement new methodologies, collaborate with external scientists and regulators through publications and presentations, and identify new areas where Statistical Innovation can add value.

Requirements:

• PhD in Statistics or related field, with experience in academic research and/or clinical drug development.
• Proven ability to deliver innovative statistical solutions, especially in areas like clinical study design, real-time data analysis, Missing Data, Estimands, Subgroups, and Biomarkers.
• Strong programming skills in R and/or SAS.
• Excellent collaborative skills across global teams.
• Research record with publications in reputable statistical journals.
• Desire to apply scientific expertise to practical, patient-benefiting problems.
• Knowledge in statistical methodology development related to safety data, observational studies, meta-analysis, Bayesian methods, and non-linear/mixed effect models.
• Awareness of evolving scientific and regulatory issues in statistics.