技能/経験

■ 応募資格(経験、資格等) / Qualification (Experience & Skill etc.)
【経験 / Experience】
＜必須 / Mandatory＞
• Experience working in the healthcare industry.
• Design, analyse, interpret, and publish research in Data Science, Epidemiology, Clinical Research, or Statistics.
• Experience in designing and conducting research using healthcare real-world data, such as claims, DPC, EMR/EHR including unstructured data, registries, clinical databases, or comparable healthcare datasets.
• Experience in preparing or substantially contributing to research protocols, analysis plans, study reports, manuscripts, or other scientific documentation.
• Lead members responsible for conducting clinical/epidemiologic research; analysing data; and interpreting data related to efficacy, safety, clinical effectiveness, and epidemiological assessments.
(Leadership focused primarily on scientific aspects is acceptable.)
• Prepare manuscripts in their area of expertise.

＜歓迎 / Nice to have＞
• Experience working with and analyzing unstructured data, including clinical text and other　healthcare data.
• Reviewing, assessing, and using Real-World Data to address clinical and research questions.
• Networking, integrating, and using EMR (electronic medical records) / EHR (electronic health records) data for clinical or epidemiological research.
• Using or applying bioinformatics methodologies to analyse medical data, databases, or scientific research.


【資格 / License】
＜必須 / Mandatory＞
 •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)

＜歓迎 / Nice to have＞
 •PhD in Data Science/Engineering/Pharmaceutical science/biostatistics or MD degree is desirable

【能力 / Skill-set】
＜必須 / Mandatory＞
• Apply appropriate study designs and analytical methods to observational, epidemiological, database, or pragmatic interventional studies, balancing business and scientific objectives.
• Define or assess study design, target population, data source, data handling, outcomes/endpoints, and analytical methods from an epidemiological point of view.
• Lead the interpretation of scientific data, translate findings into appropriate scientific messages, and draft relevant scientific publications.
• Take a leadership role in analysing medical evidence gaps, identifying opportunities and requirements for evidence generation, and integrating these into clear evidence plans or options within cross-functional teams.
• Assess scientific feasibility in using or integrating databases for research purposes, including consideration of data quality, coverage, and limitations.
• Have hands-on experience using SAS, R, or Python to process, analyse, and visualise healthcare, clinical, or scientific data.
Experience with SAS is preferred.
• Apply appropriate statistical and epidemiological approaches to address bias, confounding, missing data, and other data limitations, as relevant to the research question.
• Identify, evaluate, and implement fit-for-purpose AI and generative AI use cases within evidence-generation and data-science workflows.
• Apply AI, including generative AI, to improve the efficiency, quality, reproducibility, and scientific value of data-science work, beyond simplifying routine tasks.
• Manage projects through planning, execution, and assessment, and apply appropriate tools, frameworks, and concepts to improve the effectiveness and performance of project teams.
• Demonstrate strong communication and interpersonal skills to enable effective scientific leadership, coaching, and collaboration with internal stakeholders, external experts, and cross-functional teams.


＜歓迎 / Nice to have＞
• Practical experience using generative AI tools, such as ChatGPT, Claude, Gemini, or equivalent approved tools, in research, analytics, literature review, scientific writing, coding, text analysis, or knowledge-management workflows.
• Develop prompts or reusable workflows to obtain accurate, useful, and appropriately reviewed outputs from generative AI.
• Experience in applying appropriate human review, output-quality assessment, confidentiality, and data-governance considerations when using generative AI.
• Apply Health Technology Assessment to clarify drug characteristics and value.


【語学 / Language】
＜必須 / Mandatory＞
 日本語 Japanese：
• Read/write scientific documents including data speculation in Japanese
• Communicate/discuss IT/bioinformatics topics with the key stakeholders and experts in Japanese practically

英語 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 a English presentation  leading and facilitating research discussions in the global meetings

【その他 / Others】
＜必須 / Mandatory＞
• Develop training plans or guidance for analytical applications, tools, or workflows, as needed.
• Harmonise processes with Global teams.
• Communicate with external experts, data partners, and internal IT/digital stakeholders to identify suitable analytical or computing environments within AstraZeneca K.K.
• Continuously learn and apply new methodologies and knowledge related to epidemiology, healthcare data, machine learning, neural networks, AI, and generative AI, as relevant to the role.

＜歓迎 / Nice to have＞
• Experience sharing best practices, providing training, or supporting adoption of new analytical methods, digital tools, or AI solutions within a team or organisation.