Senior Scientist–Lead Scientist for AI-Enabled Bioinformatics and Computational Biology

Senior Scientist–Lead Scientist for AI-Enabled Bioinformatics and Computational Biology

Full Time

JR-10510

10% - Communicate scientific findings and report program performance. Prepare quarterly and annual reports, model-performance summaries, decision-support dashboards, data visualizations, technical presentations, standard operating procedures, software documentation, and progress updates for project leadership, collaborators, funding organizations, and other stakeholders. Monitor and report key performance indicators such as AI prediction accuracy, target-validation rate, model generalizability, data and pipeline throughput, construct success, and the performance of computationally selected events and trait stacks.

Lead or contribute to peer-reviewed manuscripts, conference presentations, invention disclosures, grant proposals, data releases, and open-source software or analytical resources, as appropriate, while clearly communicating model assumptions, limitations, uncertainty, and reproducibility. Other duties as assigned.

30% - Lead AI-enabled gene-target and pathway discovery. Mine, integrate, and interpret public and private biological datasets, including cotton and comparative plant genomes, pangenomes, transcriptomes, proteomes, metabolomes, genetic variation, functional annotations, phenotypes, environmental data, and scientific literature. Apply gene-network inference, feature attribution, association and causal-inference methods, comparative genomics, and multi-trait ranking to identify and prioritize genes, regulatory nodes, biochemical pathways, enzyme variants, and trait stacks associated with fiber development and quality, pigment biosynthesis, stress resilience, nutrient and water-use efficiency, plant architecture, carbon allocation, and pest resistance.

Document the biological rationale, confidence level, potential trade-offs, off-target effects, and recommended validation experiments for each candidate.

25% - Develop, validate, and operationalize advanced computational models and pipelines. Design and implement machine-learning, deep-learning, foundation-model, protein- and sequence-modeling, metabolic-network, and predictive-genomics approaches that support gene discovery, pathway optimization, construct design, and phenotype prediction. Develop digital design-of-experiments and multi-objective optimization methods that prioritize the smallest, most informative sets of edits and constructs while considering biological feasibility, fiber performance, agronomic outcomes, and sustainability metrics.

Benchmark models using independent or held-out data; evaluate accuracy, calibration, uncertainty, generalizability, interpretability, and bias; track computational predictions through experimental validation; and deploy reproducible workflows using version control, workflow managers, containers, high-performance computing, or cloud resources.

20% - Translate computational findings into cross-functional research decisions. Serve as the lead bioinformatics and computational-biology resource for the Plant Molecular Biology and DNA Engineering, Plant Transformation and Tissue Culture, greenhouse, phenotyping, fiber and materials testing, sustainability, and field-validation teams. Convert prioritized genes and pathways into construct specifications, guide edit and construct selection, advise on experimental design and statistical analysis, define data-collection requirements, and interpret molecular, transformation, plant-development, phenotype, metabolite, fiber, and agronomic results.

Lead computational reviews within the design-build-test-learn cycle, use experimental outcomes to refine models and candidate rankings, communicate risks and decision criteria, and provide technical direction and mentoring to scientists, students, analysts, and collaborators using bioinformatics tools and data products.

15% - Coordinate cross-functional program execution and construct delivery. Partner closely with Plant Tissue Culture and Transformation, AI and bioinformatics, greenhouse, phenotyping, fiber-testing, and program teams to translate research objectives into…