Jobs / Canada / Astrazeneca Pharmaceuticals Lp

Astrazeneca Pharmaceuticals Lp

Senior Real World Data Scientist

Canada - Mississauga Posted today

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Skills this job asks for

Electronic health recordsStatisticsMachine learningSQLPython

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No sponsorship evidence yet

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No salary bar for this route
Canada has no single salary bar: the employer usually needs a positive LMIA. Pay at or above the provincial median wage + 20% (e.g. C$36.92/h in Ontario) puts it in the high-wage stream. Source: https://www.canada.ca/en/employment-social-development/services/foreign-workers/median-wage.html, rules effective 2026-07-17.

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Sponsorship unknown

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About the role

This is what you will do:   The Epidemiology and Real‑World Science team in Alexion–AstraZeneca’s Rare Disease Unit accelerates patient access to innovative medicines by advancing the scientific use of Real World Data (RWD). In this role, you will apply RWD to support the rare disease portfolio, aligned with AstraZeneca best practices and standards. Building on AstraZeneca’s strong Real World Evidence (RWE) foundation and internal data assets, the team is expanding through a dedicated Real World Data Science capability. Successful candidates will tackle challenging problems with innovative methods to deliver Real World Insights and Evidence for stakeholders in Clinical Development, Regulatory, and Patient Safety. The ideal candidate is a curious, self‑directed learner with a proven record of deriving value from routinely collected healthcare or observational data, delivering health analytics across Public Health, Pharmaceutical R&D, Commercial/Payer, and Patient Safety. You will provide methods expertise, lead and support analyses of RWD, and guide study design, data source selection, and best practices in RWD utilization, emphasizing patient safety. You will also help shape Alexion/AZ’s RWS data strategy by supporting due diligence on data vendors and offering informatics support for Therapeutic Area data acquisitions. You will be responsible for:   • Deliver/Implement and support advanced secondary analyses of data from EMR, claims and primary observational data required by Therapeutic Area (TA) RWE strategies  • Comfortable working in a rapid analytics environment alongside stakeholders to ideate appropriate/impactful analysis along with implementation  • Provide clear technical input, options and directions to support strategic decisions made by AZ observational study teams on study design, data partner selection and best practices in RWE data utilization  • Monitor work performed by contract personnel and vendors   • Maintain a strong insight into the capabilities of potential external partners in RWE, especially for US and emerging markets.  • Demonstrate best practice in Real World Data Science across multiple domains, and/or stakeholder groups.  You will need to have:   • Minimum of master’s degree in Biostatistics, Epidemiology, Public Health, Statistics, Mathematics, Data Science, or related field with at least 3 years in the pharmaceutical industry, biotechnology, or consulting environment.   • The duties of this role are generally conducted in an office environment.  As is typical of an office-based role, employees must be able, with or without an accommodation to: use a computer; engage in communications via phone, video, and electronic messaging; engage in problem solving and non-linear thought, analysis, and dialogue; collaborate with others; maintain general availability during standard business hours.   We would prefer for you to have:   • This is a hands-on role – so be excited to code!   • Enthusiastic about building on and learning new methods and ways to use real world data to change the practice of medicine  • Demonstrated ability to build long-term relationships with stakeholders, understand relevant scientific/business challenges at a deep level and translate into a programme of data science activities to deliver value to the business  • Hands-on experience with EMR, disease registries, and/or insurance claims databases  • Experience with in clinical data standards, medical terminologies and controlled vocabularies used in healthcare data and ontologies (ICD9/10/SNOMED)  • Experience in supporting pharmacoepidemiology studies with proven track record of advancing approaches with statistics/machine learning/data science  • Ability to lead & handle multi-disciplinary data science projects

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