Bioinformatics Manager

Fairfield, OH - USA

Monthly Salary: Not Disclosed

Posted on: 16 hours ago

Vacancies: 1 Vacancy

Job Summary

Job Description

Contact: Neisha Camacho/Terra Parsons -
No 3rd party candidates

Job Summary:

The AI Computational Biologist will be a key contributor in developing and applying AI models for target discovery mechanism elucidation and drug repurposing while integrating outputs with wet-lab validation and preclinical research.
Youll collaborate across disciplines from ML engineers building/working with foundation models to biologists running assays ensuring that computational insights translate into tangible therapeutic hypotheses.

This role is ideal for someone who combines deep biological expertise with fluency in modern AI architectures and whos passionate about leveraging LLMs and GNNs to accelerate translational discovery.

Key Responsibilities:

AI & Computational Modeling

Design train and implement LLM- and GNN-based models to extract biological relationships from multi-modal data (omics literature chemistry clinical).
Integrate knowledge graphs and structured biomedical databases to support hypothesis generation for novel targets and mechanisms.
Collaborate with ML teams to fine-tune and evaluate models on domain-specific tasks such as genedisease association pathway prediction and compound efficacy modeling.

Biological Interpretation & Target Discovery

Apply AI-driven insights to identify prioritize and validate new drug targets and therapeutic hypotheses.
Design in silico analyses to support mechanism-of-action elucidation biomarker discovery and patient stratification.
Collaborate with wet-lab teams to translate computational predictions into experimental designs ensuring seamless handoff between in silico and in vitro/in vivo validation.

Data Integration & Curation

Integrate large-scale datasets from public and proprietary sources (e.g. transcriptomics proteomics CRISPR screens literature corpora).
Curate structured datasets for LLM fine-tuning knowledge graph expansion and GNN training.

Collaboration & Cross-Functional Impact

Partner with drug discovery data science and AI engineering teams to align modeling objectives with biological relevance.
Contribute to multi-disciplinary project teams driving programs from discovery through preclinical proof-of-concept.
Communicate computational findings clearly to both scientific and non-technical stakeholders.

Education:

PhD or MS with 5 years of relevant experience in Computational Biology Bioinformatics Systems Biology Computer Science or a related discipline.

Core Competencies:

Proven experience in target identification and translational discovery from in silico analysis to preclinical validation.
Strong understanding of molecular biology pharmacology and disease biology.
Hands-on experience developing or applying AI/ML models to biological problems especially LLMs GNNs or multi-modal integration architectures.
Prior involvement in wet-lab collaboration (assay design data interpretation or experimental validation) preferred.

Technical Skills:

Programming: Expert in Python (pandas PyTorch TensorFlow scikit-learn Hugging Face PyTorch Geometric).
AI/ML Expertise: Proficiency in LLMs GNNs transformers and model fine-tuning workflows.
Bioinformatics Tools: Familiar with databases such as Ensembl UniProt ChEMBL DrugBank GEO and OMIM.
Data Integration: Experience with multi-omics data fusion and biomedical knowledge graphs.
Visualization & Communication: Skilled in building interpretable visualizations and clearly communicating computational findings.
Version Control: Proficient in Git and collaborative coding practices.
Familiarity with molecular modeling chemoinformatics or AI for proteinligand interaction prediction.
Experience in biomedical NLP scientific literature mining or ontology construction.
Understanding of preclinical pharmacology or toxicogenomics.
Experience working in cloud environments (GCP AWS).

Soft Skills:

Deep curiosity and excitement about connecting AI architectures with biological meaning.
Excellent cross-disciplinary communication able to converse equally well with AI engineers and biologists.
Self-directed detail-oriented and comfortable working in a fast-paced dynamic startup environment.
Passionate about improving patient outcomes through innovative science and technology.

Required Experience:

Manager

Job DescriptionContact: Neisha Camacho/Terra Parsons -No 3rd party candidatesJob Summary:The AI Computational Biologist will be a key contributor in developing and applying AI models for target discovery mechanism elucidation and drug repurposing while integrating outputs with wet-lab validation and...