Manager/AD Biomarker & Translational Statistician

The Opportunity

A growing, commercial-stage biopharmaceutical company is seeking a Manager/AD Biomarker & Translational Statistician to join its Biostatistics organization. This individual contributor will operate at the intersection of biostatistics, translational science, clinical development, and data science, helping transform complex biological and clinical data into actionable insights.

The successful candidate will provide statistical leadership across translational research, biomarker strategy, multi-omics, RNA sequencing, and early clinical development. The role will be particularly well suited to a statistician who enjoys working with high-dimensional datasets and partnering closely with multidisciplinary scientific teams.

Title and scope may be adjusted based on the candidate's experience.

Key Responsibilities

• Develop and apply advanced statistical and machine-learning methods to support biomarker discovery, translational strategy, and clinical evidence generation.
• Lead or contribute to the analysis of high-dimensional data, including RNA-seq, transcriptomics, genomics, proteomics, metabolomics, and other multi-omic datasets.
• Provide statistical expertise for biomarker identification, validation, patient stratification, and treatment-response analyses.
• Integrate molecular, biomarker, clinical, and real-world datasets to generate insights that inform development strategy and scientific decision-making.
• Apply statistical methods to PK/PD biomarkers, biobank data, and genomic datasets linked to electronic health records.
• Contribute to the design and analysis of early clinical development studies, with an emphasis on exploratory and translational objectives.
• Apply statistical and causal-inference methods to observational and real-world data, including claims, electronic health records, registries, and biobank-linked datasets.
• Support clinical study protocols, biomarker analysis plans, Statistical Analysis Plans, data-review activities, and interpretation of study results.
• Build or contribute to scalable analytical pipelines that transform complex, high-dimensional datasets into analysis-ready formats.
• Partner closely with colleagues across Translational Science, Clinical Development, Data Science, Bioinformatics, Real-World Evidence, and Clinical Pharmacology.
• Clearly communicate complex statistical findings to scientific and clinical stakeholders, including colleagues without formal statistical training.
• Contribute to scientific publications, conference presentations, regulatory interactions, and other external communications.

Required Qualifications

• Ph.D. in Biostatistics, Statistics, Data Science
• At least two years of relevant industry, academic, postdoctoral, internship, or co-op experience. Candidates with approximately three to five years of directly relevant experience are particularly encouraged to apply.
• Demonstrated experience analyzing translational, biomarker, multi-omic, or RNA-seq data.
• Strong knowledge of statistical modeling for high-dimensional biological and clinical datasets.
• Proficiency in R and SAS, along with working knowledge of Python.
• Experience using statistical or machine-learning libraries and frameworks such as scikit-learn, PyTorch, or TensorFlow.
• Ability to develop reproducible analytical workflows and prepare complex datasets for statistical analysis.
• Strong communication skills, with the ability to translate technical analyses into clear scientific and business recommendations.
• Demonstrated ability to collaborate effectively across statistical, clinical, translational, and scientific functions.

Preferred Qualifications

• Experience with RNA sequencing, transcriptomics, genomics, proteomics, metabolomics, or integrated multi-omics analyses.
• Familiarity with differential expression, pathway enrichment, feature selection, dimension reduction, clustering, predictive modeling, or patient-subgroup identification.
• Experience supporting biomarker discovery, validation, or clinical implementation.
• Knowledge of human genetics methods, including GWAS, polygenic risk scores, sequencing analyses, or genetically informed target evaluation.
• Experience with PK/PD biomarker data or biobank-linked genomic datasets.
• Familiarity with real-world data sources, including electronic health records, claims databases, registries, or observational cohorts.
• Knowledge of causal-inference methods and their application to non-randomized studies.
• Experience supporting clinical trial design and analysis, particularly within early development.
• Understanding of regulatory expectations for biostatistics, biomarker analyses, and clinical evidence generation.

Ideal Candidate Profile

The ideal candidate is a scientifically curious and highly collaborative statistician who can move comfortably between exploratory research and rigorous clinical analysis. This individual should be able to work independently, manage multiple analytical priorities, and influence decisions through thoughtful statistical reasoning.

A strong candidate will bring a combination of:

• Translational and biomarker statistical expertise
• Hands-on RNA-seq or multi-omics analysis experience
• Strong programming and data-management capabilities
• Experience working with cross-functional scientific teams
• The ability to communicate complex analyses with clarity and practical relevance

Location and Working Model

This is a hybrid position based in the greater Philadelphia area, with an expectation of three days per week in the office and two days working remotely. Limited travel may be required

Compensation and Benefits

The anticipated base salary range for this position is $150,000 to $210,00, depending on qualifications, education, experience, skills, business needs, and market considerations.