Associate Director Biostatistics

Job Description Summary
Within the Quantitative Sciences drug development function, the Advanced Methodology and Data Science pillar plays a crucial role in enhancing analytical capabilities to support drug development and regulatory decision-making. This Associate Director position focuses on optimizing approaches for data-driven decision-making, utilizing clinical study data along with other internal and external data sources.

Key Responsibilities:
• Advanced Modeling & Simulation: Develop and apply sophisticated models to optimize study design, analyze complex datasets, and enhance predictive capabilities.
• Innovative Analytical Methods: Explore and implement novel statistical techniques such as Bayesian modeling, Statistical Learning, Machine Learning, and Causal Inference.
• Rapid-Response Analyses: Conduct flexible, rapid-response analyses to address emerging scientific and regulatory questions.
• Cross-Functional Collaboration: Work closely with Biostatistics, Pharmacometrics, Statistical Programming, and drug development teams to enhance study design, data interpretation, and evidence generation strategies.
• Problem Identification and Solution Development: Identify recurring problems and use a combination of reproducible research techniques and tool development to provide general solutions that can be applied repeatedly.
Quantitative Science Education: Play a key role in quantitative science education and awareness efforts.

Job Description

Location: United Kingdom

Within the Quantitative Sciences drug development function, the Advanced Methodology and Data Science pillar plays a crucial role in enhancing analytical capabilities to support drug development and regulatory decision-making. This Associate Director position focuses on optimizing approaches for data-driven decision-making, utilizing clinical study data along with other internal and external data sources.

• Advanced Modeling & Simulation: Develop and apply sophisticated models to optimize study design, analyze complex datasets, and enhance predictive capabilities.
• Innovative Analytical Methods: Explore and implement novel statistical techniques such as Bayesian modeling, Statistical Learning, Machine Learning, and Causal Inference.
• Rapid-Response Analyses: Conduct flexible, rapid-response analyses to address emerging scientific and regulatory questions.
• Cross-Functional Collaboration: Work closely with Biostatistics, Pharmacometrics, Statistical Programming, and drug development teams to enhance study design, data interpretation, and evidence generation strategies.
• Problem Identification and Solution Development: Identify recurring problems and use a combination of reproducible research techniques and tool development to provide general solutions that can be applied repeatedly.
  Quantitative Science Education: Play a key role in quantitative science education and awareness efforts.

Key responsibilities:

Project Level:

• Lead project support with hands-on expertise in novel quantitative approaches, including innovative clinical trial design, quantitative decision-making, and incorporating big data evidence (e.g., biomarkers, digital technology, real-world evidence).
• Foster collaborative relationships and effective communication with Biostatistics, Pharmacometrics, and development teams to promote and implement innovation.
• Provide agile consulting, guidance, and non-standard exploratory analysis for urgent, unplanned problems.
• Implement and communicate innovative quantitative methods to support regulatory submissions and health authority meetings.

Development Unit/ Enterprise Level:

• Support advanced analytical methods to improve decision-making at the development unit level, including key development decision points, project transitions from research to development, dose-finding, clinical development plans, target product profiles, portfolio evaluations, and in-licensing assessments.
• Develop presentations on the use of advanced quantitative methodologies.

Scientific Innovation and Training:

• Develop new quantitative methods to improve drug development across multiple projects, focusing on clinical trial design, modeling, visualization, machine learning and good data science practice.
• Evaluate the need for new technology, scientific software, visualization tools, and computational approaches to enhance efficiency and quality in drug development.
• Play a key role in global clinical innovation projects, emphasizing the practical application of innovative quantitative approaches.
• Provide training on quantitative methods to colleagues in Biostatistics, Pharmacometrics, and other development functions.

External Engagement:

• Collaborate with regulatory, academic, and industry partners to advance drug development science through innovative quantitative approaches.
• Publish in peer-reviewed journals, organize sessions at professional conferences, and contribute to cross-industry work streams in external scientific working groups.
• Identify opportunities for colleagues in Biostatistics and Pharmacometrics to contribute externally

Essential Requirements:

• Ph.D. in statistics, biostatistics, data science, machine learning, computational biology or another related field
• Fluent English oral and written
• Very good communication and presentation skill
• 6 years of relevant experience with application of innovative quantitative methods from the R&D or academic setting
• Expert knowledge in a number of relevant quantitative approaches in terms of both theory and hands-on experience

Benefits and Rewards

Competitive salary, Sales incentive bonus, Pension scheme, Share purchase scheme,Health insurance, 25 days annual leave, Flexible working arrangements, subsidized dining facilities, Employee recognition scheme, learning and development opportunities.

Commitment to Diversity and Inclusion:

Novartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.

Skills Desired
Automation, Biostatistics, Clinical Trials, Computer Programming, Cross-Functional Teams, Data Analytics, Decision Making Skills, Metadata Management, Statistical Analysis