【AstraZeneca】【Medical】メディカル本部 エビデンス&オブザベーショナルリサーチ統括部, Data Scientist
About this opportunity
■ 職務内容 / Job Description
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. And all researches/analyses are planned and delivered in a way that represent cutting-edge science, methodologies, technologies, processes, and solutions in the Pharm 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 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•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
■ 応募資格(経験、資格等) / Qualification (Experience & Skill etc.)
【経験 / Experience】
<必須 / Mandatory>
•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)*
*Mandatory only for those with pharmaceutical industry experience•Make manuscript for their own specialized topics
<歓迎 / Nice to have>
•Reviewing, assessing and using Real-World Data for research purposes to address clinical/research questions•Networking, integrating and using EMR(electronic medical records)/EHR(electronic health record) data for clinical /epidemiological research•Using/applying bioinformatic methodologies to analyze medical data/database/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 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
•Programming skills in at least one of the following languages: SAS, R, or Python
<歓迎 / Nice to have>
•Develop prompt of AI to get accurate answers•Apply health technology assessments to make clear drug characteristics
【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>• 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
【勤務地 / Work Location】
Osaka or Tokyo
Date Posted
07-8月-2026
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.
Job details
How this role compares
Computed from every other active Medical Affairs role in our database, not just this employer's listings.
We currently track 924 comparable Medical Affairs roles across 130 biopharma companies.
Salary context
175 of 924 peers report a salary range (USD, annualized)
Peers share this role's job function. This posting doesn't list a seniority level, so peers aren't narrowed by seniority either -- the range below may span more levels than usual.
Where these roles are based
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Seniority mix
502 of 924 peers have a known seniority level
Therapeutic area mix
326 of 924 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
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How we calculate "similar"
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Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.