We are seeking a part-time Computational Scientist to join the Translational Medicine group at a global biopharmaceutical company.
The successful candidate will leverage advanced computational and statistical approaches to analyze large-scale, multi-modal biological datasets, uncover disease mechanisms, identify predictive biomarkers, and generate insights that support translational research and precision medicine.
Total hours/week: 5-8 hours
Fully remote.
Key Responsibilities
Multi-Omics Data Analysis
Analyze, integrate, and interpret diverse biological datasets, including bulk RNA-seq, single-cell RNA-seq, GWAS, proteomics, and other high-dimensional omics data.
Identify biologically meaningful patterns, biomarkers, and molecular signatures associated with disease biology and therapeutic response.
Statistical Modeling & Biomarker Discovery
Apply advanced statistical methods, including linear, logistic, and Cox proportional hazards models, to identify predictive biomarkers and associate molecular features with clinical and biological outcomes.
Evaluate model performance using appropriate statistical metrics (e.g., ROC-AUC, PR-AUC, calibration, PPA, NPA, OPA, concordance) and optimize biomarker thresholds for predictive performance.
Computational Pipeline Development
Enhance and maintain existing analytical pipelines while developing new computational methods and visualization tools to address evolving scientific questions.
Produce reproducible, scalable workflows for large-scale data analysis.
End-to-End Computational Analysis
Lead projects as the primary computational analyst, overseeing the complete analytical workflow from data preprocessing and quality control through normalization, statistical modeling, biomarker evaluation, visualization, and biological interpretation.
Cross-Functional Collaboration
Collaborate closely with translational scientists, biomarker experts, and experimental biologists to translate computational findings into actionable biological insights.
Present results clearly and effectively to interdisciplinary teams and internal stakeholders.
Reproducible Research
Maintain high standards of scientific rigor by documenting analytical methods, code, workflows, and results to ensure reproducibility and transparency.
Qualifications
Education
Master's or PhD in Computational Biology, Bioinformatics, Genomics, Immunology, Statistics, Data Science, or another quantitative life sciences discipline.
Experience
1–5 years of relevant experience (including postdoctoral research) analyzing large-scale biological datasets in an academic, biotechnology, or pharmaceutical environment.
Experience with batch effect detection, normalization, covariate adjustment, and analysis of complex biological and clinical data.
Technical Skills
Statistical Modeling & Predictive Analytics
Strong expertise in multivariate statistical modeling, including linear, logistic, and Cox regression.
Experience designing and analyzing observational studies with appropriate confounder adjustment, stratification, and covariate handling.
Knowledge of predictive model evaluation, calibration, discrimination metrics, and biomarker threshold optimization.
Programming
Advanced proficiency in R (including Bioconductor, ggplot2, and related packages) and/or Python (including Scanpy, AnnData, Pandas, NumPy, and scikit-learn).
Genomics & Translational Data Analysis
Experience working with high-dimensional genomics and clinical datasets, including bulk and single-cell RNA sequencing, GWAS, proteomics, circulating tumor DNA (ctDNA), and NGS-based clinical assays (e.g., F1CDx, F1LCDx).
Ability to evaluate multiple biomarkers and integrate genomic, molecular, and clinical data to generate biologically meaningful insights.
Computational Environment
Proficiency working in Linux environments, high-performance computing (HPC) systems, and version control platforms such as Git.
Experience developing reproducible computational workflows and managing large-scale data analysis pipelines.
