Sr. Staff, Quantitative Sciences

About the role

About N-Power Medicine N-Power Medicine aims to establish a new paradigm in drug development by reinventing the 'how' and transforming clinical trials through better integration with clinical practice, ensuring broader participation by physicians and patients. We are building an exceptional multi-disciplinary team with diverse expertise spanning healthcare, engineering, technology and regulatory, and with people who share our core value of Empowering Community through generosity, curiosity and humility. We are working with urgency to bring better therapies to patients faster. Position Overview The Sr. Staff, Quantitative Sciences is a key leader in the Quantitative Sciences (QS) team and is responsible for ensuring that rigorous and fit-for-purpose statistical designs and analyses are performed to meet NPM's vision and product claims. Working with a multidisciplinary team, this role will have the opportunity to fundamentally change the "how" of oncology clinical development. This responsibility includes working in close collaboration with other QS experts, Clinical Science, Product, Oncology Data Specialist (ODS)-certified professionals and other cross functional partners to conduct next generation external control arms (ECAs) that integrate retrospective and prospective real-world data with single arm and randomized clinical trials. Through this collaboration, the Biostatistics leader also works cross-functionally to develop and ensure adherence to appropriate processes for data collection/ingestion, harmonization, standardization, quality control, management, analysis and reporting. We are open to hybrid on-site in Redwood City, CA or Remote. Role Objectives and Responsibilities In collaboration with QS leaders, builds expert capabilities in prospective ECAs and hybrid controls combining retrospective and prospective real world-data with single arm and randomized controlled trials. Provides expertise for developing statistical methodologies that mitigate, observed and unobserved, selection, confounding, and measurement bias in external control and hybrid control studies utilizing retrospective and prospective observational data. Guides and mentors internal teams on novel methodologies, best statistical practices, innovative clinical trial designs, statistical inference, and reporting statistical results. Supports key strategic collaborations with life science partners. Collaborates with the Data and Technology cross functional partners to define data requirements and data collections processes to enable next generation ECAs. Leads or supports research study design and end-to-end delivery, in particular: Study scoping, including sample size calculations and identification of data needs. Design elements, and approach to bias mitigation in the design and analysis phases. Development of study protocols and statistical analysis plans. Leads or supports the execution of the analysis plan, including the development of adjacent algorithms and other scalable tools. Leads the communication of novel methodology developed in the course of the study. Co-authors communications of study results in peer-reviewed publications, presentations, and health authority reports. Participates in internal, cross-functional discussions on data and platform strategy, and workflow development. Champions the development of tools, algorithms, and robust pipelines in support of study delivery, product offerings, data improvements, and efficiency, scalability, and reproducibility increases. Education, Experience, Behavioral Competencies, & Skills PhD in Statistics, or Biostatistics, with 6+ years of relevant experience in oncology clinical trials, biotech, and statistics. 6+ years experience in applying statistical methods to oncology patient data, including methods of advanced analytics and high dimensional data analysis. 3+ years as a Project Data Science Lead in a cross-functional clinical development environment. 2+ years in oncology clinical trial statistics. Expert knowledge of theoretical and applied epidemiology and statistics, including inferential methods for time-to-event analysis, survival analysis, and causal inference methods. Expertise in design and analysis of externally controlled studies and hybrid controls including propensity score methods, endpoint measurement bias correction methods, and external patient borrowing bayesian abd frequentest methods. Fundamental understanding of clinical- and efficacy endpoints used in oncology development. Experience collaborating with clinical-, biomarker-, and imaging scientists, to apply statistical methods to oncology drug development. Extensive experience with programming in R, SAS, and/or Python. Strong communication and collaboration skills (including statistical consulting skills, and interpersonal skills). Ability to work and collaborate in a fast paced, start-up environment. Entrepreneurial, credible and creative spirit capable of influencing stakeholders to advance th

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