Causal Inference Scientist for Translational Genomics

Ayass BioScience, LLC; Frisco (TX); On-site; USD 110,000 - 160,000; Full time

Job summary

Ayass Bioscience LLC in Frisco, Texas, is hiring a Causal Inference Scientist for BiRAGAS. You will move analyses from association toward defensible causation in gene-regulatory and disease contexts, collaborating with the founder, molecular biology, and engineering teams.

You will design and validate causal discovery methods on bulk and single-cell data, implement graph-based and structural models, and translate results into testable hypotheses for wet-lab validation.

Qualifications

• PhD in statistics, biostatistics, computational biology, computer science, or related field.
• Deep knowledge of causal inference methods and do-calculus.
• Hands-on experience with transcriptomic data (RNA-seq, scRNA-seq).
• Strong Python skills; familiarity with PyTorch or JAX and causal libraries.
• Ability to explain causal assumptions clearly to scientists and non-specialists.

Responsibilities

• Design, implement, and validate causal discovery and effect-estimation methods on transcriptomic data.
• Contribute to computational causal-modeling components of the BiRAGAS platform.
• Design analyses that withstand scientific and regulatory scrutiny with robustness checks.
• Develop benchmarks and evaluation criteria for causal claims and evidence strength.
• Translate model outputs into testable hypotheses for wet-lab validation and clinical collaborators.
• Collaborate with bioinformatics and data science teams; contribute to publications and materials.

Skills

Causal inference

Python programming

Biostatistics

Communication to non-specialists

RNA-seq analysis

Education

PhD in statistics/biostatistics/computational biology or related field

Tools

DoWhy

causal-learn

CausalNex

PyTorch

JAX

Job description

Ayass Bioscience LLC in Frisco, Texas, is hiring a Causal Inference Scientist for BiRAGAS. You will move analyses from association toward defensible causation in gene-regulatory and disease contexts, collaborating with the founder, molecular biology, and engineering teams.

You will design and validate causal discovery methods on bulk and single-cell data, implement graph-based and structural models, and translate results into testable hypotheses for wet-lab validation.