【AstraZeneca】【Medical】ﾒﾃﾞｨｶﾙ本部 ｴﾋﾞﾃﾞﾝｽ&ｵﾌﾞｻﾞﾍﾞｰｼｮﾅﾙﾘｻｰﾁ統括部, Senior Data Scientist Full-time

Lead Data Scientist

A lead data scientist (DS) is an expert role in real world data (RWD) analysis to provide scientific expertise in evidence generation so that key research questions are sufficiently addressed and set target population for research. All researches/analyses are planned and delivered in a way that represent cutting-edge science, methodologies, technologies, processes, and solutions in the pharmaceutical industry. Lead data scientist reports to director of data science, medical. Lead/co-lead observational/database research and data analysis including market/R&D analysis. Assess study design/target population/data source/data handling/analytical method and give clear inputs from an epidemiological point of view. Oversee vendors and manage timeline, quality of outputs, resource, & cost of their responsible researches. Survey & assess the necessary information for database and recommend database that fits for research/analytical purpose. Develop AI use fit for purpose. Implement AI (including generative AI) to their work process not only for simplifying DS regular tasks but also for advancing them. Support for PARCS led by pharmacovigilance department.

Design, analyze, interpret and publish researches for data science/epidemiology/clinical/statistics. Manage, and/or lead group of members who are responsible for conducting clinical/epidemiologic research, analysis of data, reading of the data for efficacy, safety, clinical effectiveness and epidemiological assessments (acceptable even if the leadership is primarily focused on the scientific features). Make manuscript for their own specialized topics.

Master's degree in public health or equivalent (individuals holding data science/engineering/pharmaceutical science/biostatistics degree are acceptable, but should have had the sufficient experience specialized in clinical development/epidemiological research). PhD in data science/engineering/pharmaceutical science/biostatistics or MD degree is desirable.

Apply appropriate study design & analytical methods to observational/epidemiological/pragmatic interventional studies to combine business and scientific agenda. Lead the interpretation of the scientific data, the translation to the appropriate messaging and drafting manuscript of relevant scientific publications. Take a leadership in analyzing medical evidence gap, spotting opportunities/requirements for evidence generation and integrate them into a clear evidence plan/option in the cross-functional team. Assess scientific feasibility in using/integrating databases for the research purposes. Develop AI for making efficient way for daily work. Manage project in planning, execution, and assessment, and apply the tools/frameworks/concepts to drive the effectiveness/performance of project teams. Solid communication and interpersonal skills to enable effective leadership, coaching and collaborations.

Develop prompt of AI to get accurate answers. Apply health technology assessments to make clear drug characteristics.

Japanese: Read/write scientific documents including data speculation in Japanese. Communicate/discuss IT/bioinformatics topics with the key stakeholders and experts in Japanese practically. English: Read/write scientific documents including data speculation in English. Communicate, and discuss IT/bioinformatics topics with the key stakeholders and experts in English practically. Make an English presentation leading and facilitating research discussions in the global meetings.

Make training plan about applications. Harmonize process with global. Communicate with external experts to search for suitable computer environment in AZ KK. Learn new methodology and knowledge with respect to machine learning and neural network.

Career level: E. Work location: Osaka or Tokyo.