Senior Real-World Data Analytics Consultant/Senior Data Scientist Consultant (Remote)

Das ist der Job

ClinChoice is searching for a Senior Real-World Data Analytics Consultant to join one of our clients.

Darum lohnt es sich

Our team says it’s the best of both worlds. As we expand our presence and offerings, we’re looking for an experienced Senior Real-World Data Analytics Consultant to join our dynamic team and lead analytical efforts across diverse therapeutic areas for a high-profile sponsor.

Required Experience Leadership of RWD analysis strategy and execution Lead development of technical specifications and study methodology Statistical programming 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 Technical Expertise Proficiency in SAS or R & SQL; expectation to be programming independently, creating packages, taking requirements, writing specifications, working with complex data structures and study design.

Knowledge of US/international data sources.

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 Tracking and updating work in software (Jira or ADO) Minimum Qualifications Master’s degree in Biostatistics, Epidemiology, Data Science, Bioinformatics, or related field with 5‑8 years of post‑graduation experience, or PhD with 3+ years post‑graduation experience.

Are you interested in working directly for a single sponsor while having the security and additional career opportunities that working for a global CRO can bring?

ClinChoice is a leading global CRO, dedicated to supporting clinical trials and real-world evidence research with a focus on exceptional quality, career development, and a supportive culture.

Core Function Description 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.

Experience in more complex programming such as propensity score analysis, lines of therapy, Sankey diagram, machine learning. Experience with complex statistical programming such as propensity score matching. Experience applying machine learning methods (LASSO, DT, RF, 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 (Optum, MarketScan, Pharmetrics+, HealthVerity, CPRD). Experience with electronic health records: IQVIA, Flatiron, Concert AI, TriNetX.

Experience with OMOP CDM or similar common data model framework. For clinical trial analysis specifically, experience with psychometric validation. 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. Additional Requirements May Include Oncology specific: experience in oncology observational studies, Flatiron and ConcertAI, understanding of programming logic in lines of therapy.

Molecular Epi specific: cloud‑based SQL desirable; experience with Clinico‑genomic multi‑modal data (Tempus AI) or population biobank data (UK biobank). Experience and comfort with 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 methodologies, including cost analysis, burden of illness studies, and comparative effectiveness research. #J-18808-Ljbffr