CDM & Biostatistics Specialist

• Senior Clinical Data Manager with extensive experience in end-to-end clinical data management, from study setup through data cleaning to database lock

• Strong expertise in SAS- and R-based statistical programming, including ADaM datasets and TLF outputs in accordance with SAP

• A combination of biostatistics, data science, clinical interpretation, and data management

• Clinical Data Management from study setup to database lock

• EDC setup, CRF design, edit checks, validation rules

• Data validation plans, UAT, SAE reconciliation, DMP

• Data cleaning, query management, data quality, QC

• Vendor oversight: CROs, laboratories, external data providers

• GCP, GDMP, 21 CFR Part 11, GxP-compliant documentation

• SAS/R biostatistics, survival analysis, Kaplan-Meier, Cox

• TLF-/SAP-compliant analysis outputs, clinical interpretation

• Understanding of CDISC, SDTM, and ADaM interfaces

Technical Stack:

• Clinical / Standards

• EDC, CRF, Edit Checks, Data Validation Plan, UAT, SAE Reconciliation, Data Transfer Specs, Data Cleaning, Query Management, GCP, GDMP, 21 CFR Part 11, CDISC/SDTM/ADaM-nahe Workflows, ADSL/ADAE/ADLB, TLF/QC

• Statistics / Programming

• SAS, R, Python, SQL/SQLite, R Markdown, knitr, Shiny, ggplot2, survival, survminer, Bioconductor, Excel/PDF/Word Reporting

• Bioinformatics / Life Sciences

• Delivery / Engineering

• Responsibility for data management activities in clinical trials: EDC setup, CRF design, validation rules/edit checks, user roles, UAT planning and execution, and data transfer specifications

• Creation and review of key data management documents: DMP, Data Validation Plan, UAT Plan/Report, SAE Reconciliation Plan, and requirements documentation

• Coordination with CROs, laboratories, and study teams; development of R-based workflows for automated data validation, data quality, and Shiny-based review/analysis components

• Work in GCP-, GDMP-, and 21 CFR Part 11-compliant environments; focus on data integrity, traceability, and auditable documentation

• Advanced analysis of national cancer registry data under strict data quality requirements; focus on mortality, age distribution, disease stages, treatment locations, and evidence-based evaluation

• Development of interactive R Shiny dashboards for clinical and scientific stakeholders, as well as the establishment of structured biobank/web platform data flows

• Support for documentation, data integration, data validation, evidence generation, and decision support in a clinical research context

• Development of an R-based reporting library for automated Word/PDF reports, migration of existing logic to R/Python, and integration into operational processes

• QA, CI/CD, unit tests, linting, static code analysis, documentation, sprint planning, and acting as team lead

• Analysis of clinical oncology data, including breast cancer and gynecological datasets; statistical analysis using SAS and R

• Conducting Kaplan-Meier analyses, Cox regressions, and comparative analyses; creating reproducible reports, tables, listings, graphs, and Excel/PDF output

• Data integration, transformation, and quality checks for heterogeneous real-world data sources; derivation of clinically interpretable results for scientific and medical stakeholders

• Bioinformatics and theoretical biology with a focus on cancer research, transcriptomics, time-series data, alternative splicing, isoforms, gene expression, and systems biology

• Development of R/Bioconductor and Python pipelines for microarray, RNA-seq, and gene-set enrichment analyses; visualization and statistical analysis for publication