Senior Data Scientist – Cell-Free RNA Sequencing Read More

Key Responsibilities

• Lead innovation in secondary and tertiary analysis of cf-RNA sequencing data, focusing on delivering rigorous and reproducible results
• Develop and implement advanced methods for differential gene expression analysis, pathway analysis, and enrichment analysis, optimizing for accuracy and biological insights
• Build, train, test, and validate predictive models, including logistic regression, random forests, and neural networks, as well as leverage existing RNA-seq large language models (LLMs) for inference and analysis
• Design and build scalable, efficient data analysis pipelines
• Engage in hypothesis-driven research, rigorously testing and validating new methods and models
• Critically evaluate results, ensuring robust models that are applicable in real-world clinical contexts beyond academic publications
• Create compelling narratives to communicate findings to both scientific and executive audiences
• Collaborate with cross-functional teams, contributing to the company’s overall scientific and technical strategy

Qualifications

• PhD in a quantitative field with a strong focus on biological sciences (e.g., Applied Statistics, Biophysics, Computational Biology)
• Postdoctoral experience is highly desirable
• 5+ years of biotech industry experience with a proven track record of leading successful projects
• Expertise in gene expression data analysis, including count table filtering, normalization strategies, noise quantification, differential expression analysis, and dimensionality reduction
• Strong foundation in statistical principles and rigorous application; including, but not limited to, hypothesis testing, P-value corrections, Bayesian approaches, bootstrapping, and permutation testing
• Extensive experience in building, training, testing, and validating machine learning and deep learning models, including model selection based on comparative analysis and performance metrics. Proficient in feature set development (selection, engineering, etc.) and skilled in updating and performing inference with RNA-seq-specific large language models (LLMs)
• Experience with common data science infrastructure, including pipelines, clusters, databases, and feature stores. Direct experience with cloud platforms (AWS preferred) for scaling, deploying, and managing data workflows is a strong advantage
• Proficient in Python and Unix/Linux environments; additional proficiency in other languages (e.g. R, Julia, Rust) is a strong plus
• Deep scientific curiosity and a solid grasp of the scientific method, hypothesis testing, and model validation
• Passion for building predictive and prognostic models that perform effectively in real-world applications
• Independent research capabilities, with the ability to drive projects with minimal supervision
• Exceptional data visualization skills and the ability to translate complex datasets into actionable insights
• Excellent communication skills, with the ability to message both technical and executive-level audiences