With the advancement of large-scale data with different modalities and new technology, data science is becoming an integral part of R&D. It plays the important role of connecting data, insights generation with advanced methodology and the underlying scientific questions across many stages in R&D to enable data-driven decision making. This requires the development of relevant data strategy, the buildup of fit-for-purpose data platform and foundation, and applications of the most effective computational methods to address the key questions.

In this role, he/she will support the RWE scientific aspects of the project including but not limited to supporting the design, the application of quantitative methodology to operate and process structured and unstructured data, in combination of domain knowledge, to generate scientific insights. The role will work supervision from manager and senior members, and function as a scientific SME working with cross-function team members and external vendors.  

Major responsibilities

• Provide expert advice on best practices in epidemiology, data analysis and data science to internal teams and stakeholders to support clinical development
• Utilize advanced analytical skills and apply advanced methods to process, analyses and interpret complex datasets, providing actionable insights when requested by relevant projects or initiatives 
• Communicate complex data insights effectively to non-technical stakeholders
• Ensure timely delivery of RWD projects and adherence to quality standards by collaborating with cross-functional teams and external partners to align project objectives with business needs
• Work with the team to continuous seek opportunities for process optimization and improved execution of RWD initiatives and projects
• Act as a liaison between external partners and internal teams to ensure alignment and effective use of data sources and foster strong relationship with data partners to support R&D portfolio
• Work with team to identify suitable data sources, trends, patterns, and best practices in data accessibility, management, analytics, and emerging technologies to drive innovation and continuous improvement in data-related processes and methodologies.
• Support team to develop scalable data research capability by designing and implementing relevant epidemiological, statistical or data science methods to meet business needs in R&D while ensuring data privacy standard
• Support the presentation of compelling validated stories regarding complex data science aspects to AZ colleagues across R&D functions and other professionals within and outside of AZ.

Education, Qualifications, Skills and Experience

• Master's degree in Epidemiology, Statistics, Public Health, or a related field. A PhD is highly advantageous.
• Over 3/5 years of hands-on experience in execution and data analysis for RWE generation or observational studies in either industry or academia.
• Proven experience supporting epidemiology studies, with a track record of advancing methods using statistics or execution of pharmacoepidemiology studies. Solid knowledge or experience in utilizing data science methods such as machine learning or LLM is a significant advantage.
• Strong capacity to quickly learn about relevant therapeutic and disease areas, and advanced analysis methods in data science
• Ability to align RWD projects with business objectives and facilitate data-driven decision-making across various organizational levels.
• In-depth knowledge of regulatory requirements, data policies, and the healthcare system in China.
• Demonstrated capability to build and maintain long-term relationships with stakeholders, comprehending scientific and business challenges, and translating these into data science activities that add value to the business.
• Excellent communication skills, with the ability to effectively network and consult with internal and external subject matter experts.
• Strong organizational and time-management skills, essential for planning and achieving multiple work goals efficiently.
• Experience in the pharmaceutical R&D environment in China is a strong plus.