Manager - Exploratory Analysis (Hybrid)

The Manager - Exploratory Analysis provides statistical expertise to support the research and development organizations for drug discovery, target identification/verification, non-clinical and clinical biomarker exploration to characterize subgroups of patients or markers of disease progression and treatment response for precision medicine. The manager works independently with discovery researchers, translational scientists and clinicians in the design, collection, analysis and reporting of multi-dimensional biomarker data from Discovery to late stage clinical development to enable objective decision-making for each drug development program.
Major Job Responsibilities:
If assigned, working independently as a point of contact for DIVES to collaborate with translational biomarker scientists and clinicians in the design, analysis and reporting of biomarker and assay studies in association with early and late phase clinical trials. The typical analysis includes pharmacodynamic biomarker analysis, safety biomarker analysis, prognostic and predictive biomarker identification and development, bioassay and companion diagnostic test development, patient subgroup identification based on biomarkers and clinical variables.
Accountable for data integrity of the responsible statistical analysis and deliverables.
Providing expertise to design, analysis and reporting of biomarker-based clinical trials or other scientific research studies. Independently developing biomarker section of clinical trial protocols or biomarker analysis plans. Implementing sound statistical methodology in scientific investigations.
Working independently as a point of contact for DIVES to collaborate with scientists in discovery and translational science units in the design, analysis and reporting of in vitro, in vivo pharmacology studies, and human clinical trials. Identify markers and signatures from internal/external omics/imaging and other biomarker data for target identification, mechanism of action, resistant mechanism, disease progression, patient selection and stratification for precision medicine.
Independently perform statistical analyses as per the biomarker analysis plan. Independently identifying issues arising in the study design; conducting and proposing scientifically sound approaches. Evaluating appropriateness of available software for planned analyses and assessing needs for potential development of novel statistical methodology.
Fully accountable for statistics/data presentation and inference. Collaborating in publication of scientific research. Ensuring that study results and conclusions are scientifically sound, clearly presented, and consistent with statistical analyses provided. Clearly explaining statistical concepts, enabling non-statisticians and biomarker collaborators to use existing tools and interpret results better.
Minimum Qualifications:
MS (with 6+ years of experience) or PhD (with 2+ years of experience) in Statistics, Biostatistics, or a highly related field, with some applied consulting or research experience on topics related to pharmaceutical research, bioinformatics, target identification, biomarkers (highly preferred), and subgroup identification.
Experience with pharmaceutical, clinical trial and/or healthcare statistics
Preferred Qualifications:
Experience working on discovery/pre-clinical studies
Experience in Clinical biomarker modeling and analysis
Experience with Machine Learning, AI and/or predictive modeling
Other Required Skills:
High degree of technical competence and effective communication skills, both oral and written
Able to identify data or analytical issues, and assist with providing solutions by either applying own skills and knowledge or seeking help from others
Able to build strong relationship with peers and cross-functional partners to achieve higher performance. Highly motivated to drive innovation by raising the bar and challenging the status quo
Able to integrate high dimensional data from different sources. Familiar with overfitting and multiple testing control methods, such as cross-validation and random resampling techniques. Familiar with machine learning and predictive modeling methods.

Employment type
Full-time

Work arrangement
Hybrid