Senior Causal Inference & Mathematical Modeling Scientist; Hybrid Systems | Bayesian Causality Job Frisco area,Texas USA,IT/Tech

Position: Senior Causal Inference & Mathematical Modeling Scientist (Hybrid Systems | Bayesian Causality [...]

Ayass 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. This role complements our existing machine learning and bioinformatics team by owning the formal equations, priors, and inference machinery that translate biology into rigorous causal models.

This is not a standard ML role. It is a foundational modeling role at the core of the platform.

Core Responsibilities

Mathematical Formulation of the Hybrid Causal Framework

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) ∝ exp(∑ θij) ))
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

Causal Discovery Under Biological Constraints

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:
Finite sample limitations
High-dimensional gene expression spaces

Structural Equation & Effect Size Modeling

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

Evidence Integration & Causal Confidence Scoring

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

Cross-Functional Collaboration

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

Required Expertise

Mathematical & Statistical Background

PhD (or equivalent depth) in Applied Mathematics, Statistics, Physics, Computer Science, or related field

Deep expertise in:

Bayesian inference

Probabilistic graphical models

Causal inference theory

Optimization and likelihood-based modeling

Causal Inference & Modeling

Hands-on experience with:

DAGs, SCMs, SEMs

Score-based and constraint-based causal discovery

Priors over graph structures

Confounding and identifiability

Strong understanding of why purely data-driven causality fails in biological systems

Computational Skills

Experience implementing mathematical models that scale to high-dimensional data
Ability to work with ML teams without being a “black-box ML” practitioner

Strongly Preferred (but Not Required)

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

#J-18808-Ljbffr


Increase/decrease your Search Radius (miles)



Job Posting Language