Architect and implement privacy-preserving protocols for constructing representative test sets across distributed partner datasets, ensuring statistical validity while maintaining data isolation.
Create comprehensive benchmark suites covering small molecules (ADMET, solubility, permeability), antibodies (affinity, stability, immunogenicity), and RNA therapeutics (stability, delivery, off-target effects).
Develop validation strategies that assess model generalization across different experimental protocols, cell lines, species, and therapeutic indications while respecting partner data boundaries.
Systematically benchmark federated models against public datasets (ChEMBL, PubChem, PDB, Therapeutic Antibody Database) to establish performance baselines and identify gaps.
Implement time-split or proper scaffold-split validation protocols that assess model performance on prospective data, simulating real-world deployment scenarios and detecting concept drift.
Build robust MLOps pipelines ensuring complete reproducibility of federated experiments, including versioning of data snapshots, model checkpoints, and hyperparameter configurations.
Design statistically powered validation studies accounting for multiple testing, hierarchical data structures, and non-independent observations common in drug discovery datasets.
Develop comprehensive performance profiling across diverse molecular scaffolds, target classes, and property ranges, identifying systematic biases and failure modes.
Collaborate with engineering teams to integrate validation frameworks with the TuneLab federated learning platform built on NVIDIA FLARE, ensuring scalable and automated testing across partner networks.

Requirements

PhD in Computational Biology, Bioinformatics, Cheminformatics, Computer Science, Statistics, or related field from an accredited college or university
2+ years of experience in the biopharmaceutical industry or related fields, with demonstrated expertise in drug discovery and early development
Strong foundation in experimental design, statistical validation, and hypothesis testing
Experience with ML model validation, cross-validation strategies, and performance metrics
Proficiency in data engineering, pipeline development, and automation
Experience with federated learning platforms and distributed computing
Knowledge of regulatory requirements for AI/ML in pharmaceutical development
Expertise in ADMET assay development and validation
Understanding of antibody engineering and characterization methods
Familiarity with RNA therapeutic design and delivery systems
Experience with clinical biomarker validation and translational research
Proficiency in workflow orchestration tools (Airflow, Kubeflow, Prefect)
Strong knowledge of containerization and cloud computing (Docker, Kubernetes)
Publications on model validation, benchmarking, or reproducibility
Experience with GxP compliance and quality management systems
Exceptional attention to detail and commitment to scientific rigor
Strong technical writing skills for regulatory documentation
Portfolio mindset balancing rigorous validation with rapid deployment for partner value.

Benefits

Health insurance
401(k) matching
Pension
Vacation benefits
Medical benefits
Dental benefits
Vision benefits
Prescription drug benefits
Flexible benefits
Life insurance
Death benefits
Time off benefits
Leave of absence benefits
Well-being benefits
Employee assistance program
Fitness benefits
Employee clubs and activities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

privacy-preserving protocolsbenchmark suitesvalidation strategiesmodel generalizationMLOps pipelinesperformance profilingexperimental designstatistical validationhypothesis testingADMET assay development

Soft Skills

attention to detailscientific rigortechnical writing

Certifications

PhD in Computational BiologyPhD in BioinformaticsPhD in CheminformaticsPhD in Computer SciencePhD in Statistics