Remote | Translational Biology Expert — $110–$135/hour Full Time on the ground

We are sharing a specialised part-time consulting opportunity for PhD-level translational biology experts with deep expertise spanning preclinical development through early clinical stages, strong scientific judgment, and the ability to guide strategic decisions across complex drug development workflows.

This role supports an exciting collaboration focused on advancing AI-enabled scientific and clinical workflows through high-quality expertise in preclinical study design, translational reasoning, early clinical program design, quantitative pharmacology, and clinical biostatistics.

Selected professionals will support high-impact deliverables across preclinical study strategy, data interpretation, clinical program design, and quantitative analysis, helping improve overall model quality. This opportunity is especially well-suited to highly analytical life sciences professionals who are comfortable evaluating complex data packages, updating mechanistic hypotheses, and delivering clear, actionable recommendations across translational development programs.

Key Responsibilities

Professionals in this role may contribute to:

Preclinical Study Design & Execution
Design and execute in vivo studies that link molecular mechanism to disease-relevant phenotypes
Select appropriate preclinical systems including in vitro, ex vivo, and animal models with clear rationale for human translatability
Develop biomarker strategies spanning target engagement through clinical response, including practical assay and sample collection considerations
Evaluate formulation and delivery approaches for tissue access across different modalities
Troubleshoot inconclusive or negative preclinical results and recommend next steps

Preclinical Data Interpretation & Decision-Making
Build exposure-activity relationships from in vivo datasets to inform clinical predictions
Evaluate whether preclinical evidence supports drug activity at the intended site of action
Update mechanistic hypotheses as new data emerges and design experiments to resolve ambiguity
Assess early safety observations and develop hypotheses for their biological basis
Evaluate immunogenicity risk and its potential downstream consequences
Support portfolio-level decisions including advance, pivot, or terminate recommendations grounded in data quality and residual uncertainty

Early Clinical Program Design
Determine safe and pharmacologically relevant starting doses for human studies, including cross-species scaling and its limitations
Design dose escalation schemes informed by expected pharmacodynamic timecourses and safety margins
Power early-phase studies appropriately based on biological variability and expected effect sizes
Define patient selection and enrichment strategies using biomarker and epidemiological data
Select endpoints and evaluate when surrogate measures are sufficient versus when clinical endpoints are required
Plan interim analyses, safety monitoring, and adaptive decision rules

Quantitative Pharmacology & Clinical Modeling
Conduct exposure-response analysis and support model-informed dose optimization
Apply population PK and PK/PD modeling, including covariate identification and impact assessment
Support dose escalation decisions using accumulating trial data
Perform longitudinal efficacy modeling, including time-to-effect and trajectory-based analyses
Conduct sensitivity analyses addressing missing data, protocol deviations, and intercurrent events

Clinical Biostatistics
Support statistical analysis planning across binary, continuous, and time-to-event endpoints
Contribute to multiplicity-adjusted hypothesis testing and sample size determination
Evaluate subgroup and heterogeneous treatment effect analyses with appropriate false discovery controls
Address estimand-related considerations including missing data frameworks and dropout patterns
Support adaptive and interim monitoring design, including futility boundaries and alpha-spending functions

Ideal Profile

Strong candidates may have:
A PhD, MD, and/or PharmD in pharmacology, pharmaceutical sciences, biostatistics, quantitative biology, or a related field
5+ years of industry experience in pharma, biotech, or CRO environments
Direct experience supporting at least one program from late preclinical stages through IND or into early clinical development
Ability to independently evaluate complex data packages and deliver clear, actionable recommendations
Strong communication skills for technical and non-technical audiences

Preferred qualifications

Depth in one or more areas including preclinical study design, preclinical data interpretation, early clinical program design, quantitative pharmacology, or clinical biostatistics
Breadth across multiple translational development domains
Strong experience with biomarker strategy, model-informed development, and cross-functional decision support
Ability to connect preclinical evidence with early clinical planning in a rigorous and practical way

Why This Opportunity

Contribute specialised translational biology expertise to a cutting-edge AI collaboration
Help improve AI-enabled workflows across preclinical and early clinical development
Work on high-impact scientific and strategic deliverables with strong translational relevance
Flexible remote work with competitive hourly compensation

Contract Details

Independent contractor role
Fully remote with flexible scheduling
Hourly compensation of $110–$135 per hour
Open to candidates based in the United States or United Kingdom
5+ years of industry experience in pharma, biotech, or CRO environments required
Projects may be extended, shortened, or concluded early depending on project needs and performance
Weekly payments via Stripe or Wise
Work will not involve access to confidential or proprietary information from any employer, client, or institution
Please note: We are unable to support H1-B or STEM OPT candidates at this time
Start date: Immediate

About the Platform

This opportunity is available through a leading AI-driven work platform that connects domain experts with frontier AI research projects.
Experts contribute to improving advanced AI systems by providing specialised expertise across real-world workflows, structured evaluation, model training support, and domain-specific content validation.
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