Pre-Clinical Statistician

Responsibilities

• Provides technical leadership and expertise in initiating and carrying out multi-disciplinary or cross-functional projects that impact the Lilly Research Laboratory and the Statistical Sciences organization
• Leads the assessment and introduction of new statistical technology and methodology, including both frequentist and Bayesian techniques, to apply to broader practice
• Led the design and analysis of in-vitro and in-vivo experiments to advance the discovery portfolio, innovative study designs, and statistical analysis plans in collaboration with biologists and other researchers
• Provides consultation for specialized biomarker analyses including genomic analysis of RNA expression data and analysis of other biomarker data (single cell RNA, NGS, protein, imaging) in conjunction with pre-clinical and clinical data
• Develops novel visualizations to explain complex results and interactions
• Consults and provides statistical direction on priority discovery projects
• Engages in the write-up of the results and presentations to Project and Scientific Management
• Understands biological assay development needs and advises biologists on experiments required to validate biological assays for Drug Discovery programs
• Finds opportunities for meta-analysis and other data mining techniques, and then conducts them, to identify key results from a series of experiments to identify key conclusions from the gestalt of all experiments in the program and determine appropriate next steps the Project Team should prioritize to optimize progress
• Develops and provides training courses on statistical methods for biologists, chemists, and other research staff
• Potential leadership and direction of contracted statistical analyst support
• Maintains currency with respect to statistical methodology, maintains proficiency in applying new and varied methods, and to be competent in justifying methods selected
• Influences team members regarding appropriate research methods
• Performs peer-review of work products from other statistical colleagues
• Collaborates with team members to write reports and communicate results
• Affords assistance in communicating study results via internal governance, manuscripts, or oral presentations in group settings, as well as for communicating one-on-one with key customers and presenting at scientific meetings
• Responds to regulatory queries and interacts with regulators
• Understand disease states, competitive landscapes, and the foundational science in order to enhance the level of patient focus and collaboration and be seen as a strong scientific contributor
• Performs work in full compliance with assigned curriculum(s) and will be responsible for following applicable Corporate, Medical, local, and departmental policies, procedures, processes, and training as well as ensuring legal, regulatory and data protection requirements are met in all activities
• Statistical Leadership and Teamwork: Introduce and apply innovative methodology and tools to solve critical problems; Merge scientific thinking and business knowledge to identify issues, evaluate options and implement solutions; Lead projects independently and work effectively across functions; Apply technical expertise to influence business decisions

Qualifications

• Ph.D. in Statistics or Biostatistics
• Experience in oncology research is preferred
• Expertise and experience in study design, mixed models, multivariate analysis, design of experiments, multiplicity control, and analysis of variance
• Proficient in statistical programming languages/software such as R, Python, SAS®, JMP®, etc.
• Demonstrated problem-solving ability and critical thinking
• Interpersonal communication skills for effective customer consultation and collaboration
• An established track record of developing and maintaining an area of statistical or collaborative research

Additional Preferences

• Experience with pre-clinical, biomarker, genomics/genetics, or discovery research data and analyses
• Knowledge or education in Biology
• Skills in biological pathway/network analysis
• Skills in predictive modeling and machine learning
• Experience working in High-Performance Computing environment