【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, Data Scientist

■ 職務内容 / Job Description

The Data Scientist(DS) is a strategic scientific role within Medical Data Science, responsible for generating real-world evidence (RWE) to address crucial research questions across the product lifecycle and Data science perspective.

Data science research activities are planned and delivered using cutting-edge scientific approaches, methodologies, technologies, processes, and solutions within the pharmaceutical industry. And this role helps deepen understanding of unmet medical needs, treatment patterns, clinical outcomes, safety, effectivenees in real-world settings by leveraging epidemiological expertise, healthcare data, advanced analytics, and fit-for-purpose AI technologies etc.

The Data Scientist translates complex data into actionable scientific insights that inform Medical, R&D, and cross-functional decision-making. The role reports to the Director of Data Science or Sr. Data Scientist, 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 venders 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

• Identify, evaluate, and implement fit-for-purpose AI and generative AI use cases within evidence generation and data science workflows.

• Support for PARCS led by Pharmacovigilance department

■ 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.

【勤務地 / Work Location】

Osaka or Tokyo

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.