ClinChoiceRemote
Senior Real-World Data Scientist/Analytics Consultant (Clinical or pharmaceutical industry experience is required)-(Remote)
United States of America
Full-time
1 day ago
H1B Sponsor Likely
Key skills
Real World Data (RWD) analysisStatistical programmingRSASSQLPythonPropensity score analysisMachine learningLASSODecision TreesRandom ForestXGBoostOHDSI toolsetDARWIN toolsetEpidemiologyOutcomes researchStudy designBiomarker/genomic data analysisHealthcare databasesClaims data analysisElectronic Health Records (EHR) data analysisOMOP Common Data ModelPsychometric validationStatistical analysis plan developmentProtocol developmentManuscript developmentJiraAzure DevOps (ADO)Oncology observational studiesClinico-genomic data analysisCloud-based SQLTableauPower BIHealth economics and outcomes research (HEOR)Project managementAIMachine LearningAnalyticsBIStatistical AnalysisLeadershipCollaboration
About this role
ClinChoice is a leading global CRO, dedicated to supporting clinical trials and real-world evidence research. They are seeking a Senior Real-World Data Scientist/Analytics Consultant to lead analytical efforts across diverse therapeutic areas for a high-profile sponsor, ensuring quality standards and methodological rigor in Real World Data research.
Responsibilities:
- Lead development of analysis specifications, develop programs, and conduct analyses while providing technical guidance for Real World Data (RWD) research
- Ensure quality standards and methodological rigor across projects through development of patient cohorts and validation of key variables
- Leadership of RWD analysis strategy and execution
- Lead development of technical specifications and study methodology
- Statistical programing proficiency (R, SAS, SQL., Python)
- Oversight of quality control processes
- Cross-functional team collaboration
- Management of project timelines and deliverables
- Development of best practices and standards
- Demonstrated ability to communicate complex analyses to non-technical stakeholders
- Proficiency in SAS or R & SQL is a must, expectation to be programming independently, creating packages, taking requirements, writing specifications, work with complex data structures and study design
- Experience in more complex programming, such as propensity score analysis, lines of therapy, Sankey diagram, machine learning
- Experience with complex statistical programing, such as propensity score matching
- Experience applying machine learning methods (such as LASSO, DT, RF, and XGBoost) with RWD
- Experience with OHDSI or DARWIN tool sets in R
- Understanding of epidemiology / outcomes research, experience with study design and execution, Biomarker/genomic data sources
- Experience with healthcare databases:
- Claims (examples include Optum, MarketScan, Pharmetrics+, HealthVerity, CPRD)
- Electronic Health Records (examples include IQVIA, Flatiron, Concert AI, TriNetX)
- Experience with OMOP CDM or similar common data model framework
- Knowledge of US/international data sources
- For clinical trial analysis specifically, experience with psychometric validation
- Project Implementation capability (reviewing, contributing to technical review and suggesting edits, executing) in the following are expected
- Statistical analysis plan development
- Protocol / manuscript development
- Study design and execution
- Cross-functional team collaboration
- Being able to track and update work in a software (Jira or ADO)
Requirements:
- Leadership of RWD analysis strategy and execution
- Lead development of technical specifications and study methodology
- Statistical programing proficiency (R, SAS, SQL., Python)
- Oversight of quality control processes
- Cross-functional team collaboration
- Management of project timelines and deliverables
- Development of best practices and standards
- Demonstrated ability to communicate complex analyses to non-technical stakeholders
- Proficiency in SAS or R & SQL is a must, expectation to be programming independently, creating packages, taking requirements, writing specifications, work with complex data structures and study design
- Experience in more complex programming, such as propensity score analysis, lines of therapy, Sankey diagram, machine learning
- Experience with complex statistical programing, such as propensity score matching
- Experience applying machine learning methods (such as LASSO, DT, RF, and XGBoost) with RWD
- Experience with OHDSI or DARWIN tool sets in RSubject Matter Expertise
- Understanding of epidemiology / outcomes research, experience with study design and execution, Biomarker/genomic data sources
- Experience with healthcare databases: Claims (examples include Optum, MarketScan, Pharmetrics+, HealthVerity, CPRD)
- Electronic Health Records (examples include IQVIA, Flatiron, Concert AI, TriNetX)
- Experience with OMOP CDM or similar common data model framework
- Knowledge of US/international data sources
- For clinical trial analysis specifically, experience with psychometric validation
- Project Implementation capability (reviewing, contributing to technical review and suggesting edits, executing) in the following are expected: Statistical analysis plan development, Protocol / manuscript development, Study design and execution, Cross-functional team collaboration, Being able to track and update work in a software (Jira or ADO)
- Master's degree is Biostatistics, Epidemiology, Data Science, Bioinformatics, or related field with 5-8 years of relevant post-graduation experience or PhD with 3+ years post-graduation experience
- Advanced expertise in statistical programming and observational research methods
- Comprehensive experience with healthcare data sources and analysis
- Proven ability to lead projects autonomously in a matrix environment
- Track record of managing priorities and performance targets
- Oncology Specific: Experience in oncology observational studies, experience in Flatiron and ConcertAI, understanding of programming logic in lines of therapy
- Molecular Epi Specific: Cloud-based SQL is desirable
- Experience with Clinico-genomic multi-modal data (e.g. Tempus AI) or population biobank data (UK biobank)
- Experience and comfort to multitasking and working in a matrix environment
- Tableau or Power BI or other graphics tool is a plus
- HEOR Specific: SAS/SQL required, additional experience with R beneficial
- Experience with health economics and outcomes research (HEOR) methodologies, including cost analysis, burden of illness studies, and comparative effectiveness research