Senior Causal Inference & Mathematical Modeling Scientist (Hybrid Systems | Bayesian Causality | Systems Biology)

Role SummaryAyass 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 Responsibilities1. Mathematical Formulation of the Hybrid Causal FrameworkTranslate biological knowledge graphs (pathways, directional mechanisms, interventions) into formal mathematical representationsDefine 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 algorithmsEnsure mathematical consistency across graph structure learning, parameter estimation, and uncertainty quantification2. Causal Discovery Under Biological ConstraintsDesign 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 constraintsAddress known causal challenges:Markov equivalenceHidden confoundingFinite sample limitationsHigh-dimensional gene expression spaces3. Structural Equation & Effect Size ModelingDevelop and fit Structural Equation Models (SEMs) on biologically constrained graphsEstimate context-specific causal effect sizes with confidence intervalsSupport heterogeneous effects, moderators, and disease- or tissue-specific contexts4. Evidence Integration & Causal Confidence ScoringDefine the mathematical framework for integrating statistical evidence, priors, genetic evidence, and mechanistic plausibilityContribute to a composite causal confidence score that moves beyond p-values toward actionable inferenceDesign principled approaches to resolve conflicts between data-driven signals and database knowledge5. Cross-Functional CollaborationWork 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 learningRequired ExpertiseMathematical & Statistical BackgroundPhD (or equivalent depth) in Applied Mathematics, Statistics, Physics, Computer Science, or related fieldDeep expertise in:Bayesian inferenceProbabilistic graphical modelsCausal inference theoryOptimization and likelihood-based modelingCausal Inference & ModelingHands-on experience with:DAGs, SCMs, SEMsScore-based and constraint-based causal discoveryPriors over graph structuresConfounding and identifiabilityStrong understanding of why purely data-driven causality fails in biological systemsComputational SkillsStrong Python proficiency (PyMC, Stan, NumPy, SciPy, PyTorch/JAX preferred)Experience implementing mathematical models that scale to high-dimensional dataAbility to work with ML teams without being a “black-box ML” practitionerStrongly Preferred (but Not Required)Experience in systems biology, genomics, transcriptomics, or proteomicsFamiliarity with biological pathway databases (KEGG, Reactome, SIGNOR, etc.)Prior work on regulatory-facing, interpretable models in life sciencesExperience translating theory into production-grade inference pipelines