Us To Apply
Healthcare
SeniorScientist,ClinicalIntelligenceEvidence
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“Senior Scientist, Clinical Intelligence Evidence at Us To Apply. Skills: Epidemiology, Biostatistics, AI, Machine learning. Design observational studies. Deliver observational studies”
What You'll Achieve.
Address priority scientific questions; Address priority clinical development questions; Guide study design; Guide development milestones; Guide lifecycle decisions; Increase consistency; Increase efficiency
Industry & Context.
Translate findings; Address scientific questions; Address clinical development questions; Implement statistical approaches; Implement comparative effectiveness; Implement longitudinal analysis; Implement causal inference; Implement patient characterization; Implement predictive analytics; Assess data suitability; Accelerate phenotyping; Accelerate endpoint identification; Accelerate workflow automation
What They're Looking For.
Must Have
Advanced degree in epidemiology, biostatistics, data science, public health, or related discipline, Understanding of observational study design, Understanding of epidemiologic methods, Understanding of real-world data interpretation, Experience applying machine learning or AI methods in healthcare data, Practical experience with real-world data assets, Experience applying quantitative methods to support evidence generation, Programming and data analysis skills in R, Python, and/or SQL, Ability to manage and deliver multiple analyses or projects, Advanced English
Nice to Have
PhD or MSc, Experience developing reusable analytical code, tools, or workflows, Familiarity with privacy-preserving analytics, Familiarity with federated approaches, Experience working across diverse data environments, Experience with multimodal real-world data, Experience with linked datasets, Experience in Cardiovascular, Renal and Metabolic (CVRM) disease areas
What You'll Do.
Design observational studies
Deliver observational studies
Address scientific questions
Address clinical development questions
Translate findings into recommendations
Guide development milestones
Guide lifecycle decisions
Conduct data feasibility
Implement statistical approaches
Implement comparative effectiveness
Implement longitudinal analysis
Implement causal inference
Implement patient characterization
Implement predictive analytics
Work across claims data
Work across electronic health records
Work across registries
Work across linked healthcare datasets
Assess data suitability
Deploy machine learning
Accelerate phenotyping
Accelerate endpoint identification
Accelerate workflow automation
Contribute reusable code
Scout new technologies
Evaluate new technologies
Adopt new technologies
Partner with Clinical Development
Partner with Biostatistics
Partner with Data Science
Communicate technical concepts
Uphold scientific quality
Uphold reproducibility
Follow documentation best practices
Follow coding best practices
Follow quality control best practices
Evolve team capabilities
Evolve team standards
How You'll Work.
Team & Collaboration
Cross-functional team; Clinical Development; Biostatistics; Data Science; Specialist audiences; Non-specialist audiences
Communication Scope
Explain technical findings; Communicate technical concepts
Process & Methodology
Manage projects, Deliver projects
Full Job Description
Are you ready to turn complex real-world data into evidence that shapes clinical development decisions in Cardiovascular, Renal and Metabolic disease? Join us to apply epidemiology, biostatistics and AI to answer high-value scientific questions that move medicines forward. In this role, you will be part of a cross-functional team bridging Clinical Development, Biostatistics and Data Science to design and deliver observational studies at scale. Your work will inform trial strategy, refine patient selection and endpoints, and improve the speed and quality of decision-making across the CVRM portfolio. Can you see yourself translating sophisticated analytics into clear, decision-relevant insights that influence the path of our pipeline? **Accountabilities:** * Evidence Generation and Study Delivery: Design and deliver observational studies and RWE projects that address priority scientific and clinical development questions across CVRM, from protocol and analysis plan through to results interpretation and dissemination. * Decision-Ready Insights: Translate findings into clear, evidence-based recommendations that guide study design, development milestones and lifecycle decisions. * End-to-End Analytical Execution: Build and validate cohort and phenotyping definitions; conduct data feasibility assessments; implement statistical analyses; and report results with transparency and reproducibility. * Scientific and Technical Expertise: Apply robust observational study design, epidemiology and biostatistics; select and implement approaches such as comparative effectiveness, longitudinal analysis, causal inference, patient characterization and predictive analytics aligned to the research question * Real-World Data Mastery: Work across claims, electronic health records, registries and linked datasets; assess suitability and limitations; and clearly document assumptions, methods and outputs aligned to internal scientific standards. * AI-Enabled and Scalable Analytics: Deploy AI an
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