POSITION DESCRIPTION : Takeda Development Center Americas, Inc. is seeking a Manager, Statistics with the following duties: independently represent data science function on global project teams in support of pre-clinical and clinical studies and compound level programs; play a leadership role in the development and review of the study synopsis, protocol, statistical analysis plan, clinical study report, ensuring accurate and statistically valid deliverables; perform end-to-end data analyses, from hypotheses formulation, experimental design, writing analysis plans, data cleaning, executing analysis, and preparing reports and documentation; develop and support best data handling and data provenance practices utilizing reproducible, interpretable research approaches; strengthen Takeda’s advanced analytics toolkit by identifying and applying emerging techniques, as well as by advancing the state of the art and developing novel analysis tools as needed; collaborate effectively within a matrix environment, with scientists in various therapeutic areas to understand disease biology and to identify or develop appropriate goals for data science work (for example identifying sensitive and specific endpoints for segmenting patient population, tracking disease progression, quantifying quality of life, etc.) as well as with colleagues from pharmacology, chemistry, imaging, translational sciences, computational biology, experimental medicine; work closely with Takeda statisticians to ensure statistical issues in data analysis are ad-dressed as needed; anticipate and communicate internal and external resource and quality issues that may impact deliverables or timelines of the program; propose and implement solutions; escalate is-sues to management as appropriate in a timely manner; increase the internal impact of Takeda’s data science work by identifying opportunities across therapeutic areas; increase the external recognition of Takeda’s data science work by participating in conferences, publishing work and developing external collaborations. Prior experience must include: develop Bayesian hierarchical models incorporating stick-breaking processes and weights for prediction and inference; design and conduct simulations studies to evaluate statistical methods, including defining assumptions, generating data, and summarizing results; conduct independent statistical research projects, including literature review, mathematical derivations of statistical methodology, real-world data application, and preparing written reports or publications; implement reproducible statistical workflows for high dimensional data analysis and methodological evaluation using programming tools (R, Python, SAS) and UNIX operating systems (High-Performance Computing); apply statistical methods and deep learning approaches, including survival analysis, longitudinal data analysis, and deep neural network models, to evaluate hypotheses and interpret results in biomedical and clinical data to cross-functional research teams.