Ayass BioScience, LLC · 6 hours ago
Senior Causal Inference & Mathematical Modeling Scientist (Hybrid Systems | Bayesian Causality | Systems Biology)
Frisco, TX
Full-time
Hybrid
Senior LevelAyass Bioscience is building a next-generation hybrid causal inference platform (BiRAGAS) that integrates established biological knowledge with data-driven discovery to produce interpretable, regulatory-defensible causal models. We are seeking a senior scientist with deep expertise in mathematical modeling, causal inference, and Bayesian methods to design and implement the mathematical foundations of our hybrid architecture.
BiotechnologyHealth CareMedicalOnline Portals
Responsibilities
Translate biological knowledge graphs (pathways, directional mechanisms, interventions) into formal mathematical representations
Define and implement Bayesian priors over causal graph structures (e.g., ( P(G) \propto \exp(\sum \theta_{ij}) ))
Formalize constraints, forbidden edges, soft priors, and fixed anchors within causal discovery algorithms
Ensure mathematical consistency across graph structure learning, parameter estimation, and uncertainty quantification
Design and adapt constrained causal discovery algorithms (PC, GES, score-based, hybrid methods)
Incorporate biological directionality, pathway topology, genetic anchors (eQTL/pQTL), and intervention data as first-class constraints
Address known causal challenges:
Markov equivalence
Hidden confounding
Finite sample limitations
High-dimensional gene expression spaces
Develop and fit Structural Equation Models (SEMs) on biologically constrained graphs
Estimate context-specific causal effect sizes with confidence intervals
Support heterogeneous effects, moderators, and disease- or tissue-specific contexts
Define the mathematical framework for integrating statistical evidence, priors, genetic evidence, and mechanistic plausibility
Contribute to a composite causal confidence score that moves beyond p-values toward actionable inference
Design principled approaches to resolve conflicts between data-driven signals and database knowledge
Work closely with:
ML engineers (who implement scalable systems)
Bioinformaticians (who prepare and interpret omics data)
Domain scientists (who curate biological knowledge)
Act as the mathematical authority bridging biology and machine learning
Qualification
Required
PhD (or equivalent depth) in Applied Mathematics, Statistics, Physics, Computer Science, or related field
Deep expertise in Bayesian inference
Deep expertise in probabilistic graphical models
Deep expertise in causal inference theory
Deep expertise in optimization and likelihood-based modeling
Hands-on experience with DAGs, SCMs, SEMs
Hands-on experience with score-based and constraint-based causal discovery
Hands-on experience with priors over graph structures
Hands-on experience with confounding and identifiability
Strong understanding of why purely data-driven causality fails in biological systems
Strong Python proficiency (PyMC, Stan, NumPy, SciPy, PyTorch/JAX preferred)
Experience implementing mathematical models that scale to high-dimensional data
Ability to work with ML teams without being a 'black-box ML' practitioner
Preferred
Experience in systems biology, genomics, transcriptomics, or proteomics
Familiarity with biological pathway databases (KEGG, Reactome, SIGNOR, etc.)
Prior work on regulatory-facing, interpretable models in life sciences
Experience translating theory into production-grade inference pipelines
Company
Ayass BioScience, LLC
Ayass BioScience, LLC was established in 2014 with a mission to provide early disease detection, develop a disease monitoring system, and ultimately achieve better patient outcome by implementing Molecular Modern Medicine to daily clinical practice.
Founded in 2014
11-50 employees
Funding
Current Stage
Early StageRecent News
2025-05-18
2025-05-18
2025-05-18
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