Jobs / United States / Astrazeneca Pharmaceuticals Lp
【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
Astrazeneca Pharmaceuticals Lp · 🇺🇸 2 Locations
Sponsorship verdict
Sponsorship possible
One solid signal, not two — worth applying, and worth asking about sponsorship early.
- Employer is on a government sponsor recordThe US Department of Labor certified 99 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 31 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
- The posting doesn’t mention sponsorshipSilence isn’t a refusal — ask the recruiter before investing much time.
- No salary bar for this routeH-1B has no fixed salary bar: the employer must pay at least the prevailing wage for the role and area. Cap-subject employers enter a lottery weighted by wage level. Source: https://www.federalregister.gov/documents/2025/12/29/2025-23853/weighted-selection-process-for-registrants-and-petitioners-seeking-to-file-cap-subject-h-1b, rules effective 2026-02-27.
- What Astrazeneca Pharmaceuticals Lp paid sponsored hires in similar roles15 certified filings for “Associate Director Oncology Outcomes Research” (Medical Scientists, Except Epidemiologists) in MD: $126k–$185k, median $140k. Most were filed at wage level II (47%) — 2 lottery entries, ≈31% projected selection odds for cap-subject employers. Source: US Department of Labor LCA disclosure data (Oct 2025 – Jun 2026).
- Confirmed live todayWhen a source last listed this job as open.
US H-1B: cap-subject employers enter a lottery weighted by wage level — Level I gets 1 entry, Level IV gets 4 (DHS projected selection odds ≈15% at Level I to ≈61% at Level IV). Universities and non-profit research employers are cap-exempt. The $100,000 fee for new petitions from abroad is currently blocked by a court order (appeal pending).
A verdict summarises public evidence; it is not legal advice and never a guarantee — the employer and the immigration authority decide. Sign in to factor in where you can already work.
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Sponsor Radar — Astrazeneca Pharmaceuticals Lp
The US Department of Labor certified 99 H-1B/E-3 labor condition applications for this employer between Oct 2025 and Jun 2026 (latest Jun 2026) — the step every H-1B hire needs first. USCIS also records 31 H-1B approvals in FY2023. Source: LCA disclosure data (US Department of Labor (OFLC)).
Past sponsorship or register membership never guarantees sponsorship for this vacancy or for you. Full Sponsor Radar for Astrazeneca Pharmaceuticals Lp →
About the role
■ 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 e