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Job Overview:
As a Real-World Evidence Statistical Programmer, you will play a critical role in harnessing real-world data to generate insights that support the development, approval, and commercialization of innovative drugs. You will work collaboratively with cross-functional teams to inform strategic decision-making and enhance patient outcomes.
Key Responsibilities:

• Data Acquisition, Analysis and Modeling:
  • Collaborate with internal and external partners to acquire, validate, and integrate real-world data from diverse sources. Ensure data quality and integrity, and maintain comprehensive documentation for reproducibility
  • Design and implement advanced statistical models and machine learning algorithms to extract insights from various real-world data sources, including electronic health records (EHRs), claims data, patient registries, and more.
  • Analyze complex datasets to identify trends, patterns, and opportunities that can inform product development and market strategies.
• Support Real-World Studies/Health Outcomes Research:
  • Design and execute real-world studies to generate real-world evidence, supporting the safety, efficacy, and economic value of products.
  • Contribute to study design, including defining objectives, selecting appropriate methodologies, and developing analytical plans.
  • Oversee and perform data curation and cleaning processes to ensure high-quality datasets for analysis.
  • Collaborate with cross-functional teams to ensure the alignment of study objectives with business and regulatory goals.
• Insight Generation:
  • Translate complex analytical results into actionable insights and communicate findings to stakeholders, including research scientists, clinical teams, and commercial partners.
  • Develop data visualizations and reports that convey the impact of real-world data on clinical and business decisions.
• Innovation and Improvement:
  • Stay updated with industry trends and innovations in data science, machine learning, and real-world evidence.
  • Propose and develop new methodologies to improve data processing and analysis capabilities.
• Cross-Functional Collaboration:
  • Work closely with clinical, regulatory, commercial, and technology teams to ensure alignment and integration of data-driven insights into business processes.

Qualifications:

• Education:
  • Bachelor‘s/Master’s or Ph.D. degree in Data Science, Biostatistics, Epidemiology, Health Outcomes, or a related field.
• Experience:
  • Proven experience (3+ years) in data analysis, preferably in the pharmaceutical or healthcare industry.
  • Experience working with real-world data sources, such as EHRs, claims data, or patient registries.
• Technical Skills:
  • Proficiency in statistical and data analysis software such as SQL, R, SAS, Python, or similar. SQL is a must.
  • Experience with data visualization tools like Tableau, Power BI, or similar.
  • Familiarity with machine learning frameworks and techniques.
• Soft Skills:
  • Strong analytical and problem-solving skills.
  • Excellent communication and presentation abilities to convey complex information to non-technical stakeholders.
  • Ability to work effectively in a team-oriented, collaborative environment.

Preferred Qualifications:

• Experience in drug development processes or regulatory environments.
• Knowledge of healthcare systems, epidemiology, and outcomes research.

Other requirement

• May need travel and on-site data curation/analysis as required:
  • Engage with local vendor to understand data sources and ensure proper data collection methods.
  • Conduct on-site data curation and analysis to ensure immediate and accurate data handling, facilitating timely insights.