Full-Time

Bioinformatics Analyst

Cheminformatics

Posted on 1/24/2026

University of Miami

University of Miami

No salary listed

Company Does Not Provide H1B Sponsorship

Miami, FL, USA

In Person

Category
Biology & Biotech (2)
,
Required Skills
Machine Learning
biostatistics
Requirements
  • Bachelor's degree required
  • Minimum 1 year of relevant experience
  • Skill in collecting, organizing, and analyzing data
  • Ability to recognize, analyze, and solve a variety of problems
  • Ability to exercise sound judgment in making critical decisions
  • Ability to process and handle confidential information with discretion
  • Proficiency in computer software
Responsibilities
  • Runs and maintains existing next generation data analysis pipelines.
  • Assists senior bioinformatics analysts in pipeline and algorithm development and validation.
  • Extracts features of interest from data relating to specific biological questions.
  • Contributes to the maintenance of genomic and clinical databases.
  • Performs statistical and integrative data analysis on genomics and clinical data.
  • Works along-side other team members and data analysts.
  • Follows standard operating procedures, documentation, and scripts to generate reports.
  • Adheres to University and unit-level policies and procedures and safeguards University assets.
  • Process and analyze large-scale multi-omics and drug discovery datasets (WES, WGS, bulk/single-cell RNA-Seq, ATAC-Seq, Drug Screening, CRISPR Screening).
  • Develop and maintain production-grade pipelines for processing and analysis of diverse biomedical datasets.
  • Develop visualization and bioinformatic analysis tools for exploring diverse biomedical data.
  • Integrate developed tools and pipelines with Sylvester’s internal end-user data analytics platform.
  • Apply state-of-the-art AI, bioinformatics, and biostatistics approaches to cancer precision medicine.
  • Write standard operating procedures and other relevant documentation.
  • Develop and apply AI/ML models for structure-based and ligand-based drug design.
  • Utilize computational tools to predict and optimize pharmacokinetic (PK) and pharmacodynamic (PD) properties of drug candidates.
  • Design and implement algorithms to identify novel drug candidates and elucidate their mechanisms of action.
  • Integrate and analyze large-scale biological, chemical, and clinical datasets to extract insights and inform drug discovery decisions.
  • Collaborate with cross-functional teams to advance drug discovery projects.
  • Analyze and interpret PK/PD data using advanced statistical and modeling software.
  • Stay updated with advancements in computational chemistry, AI, and bioinformatics.
  • Contribute to the development of new computational tools and methodologies.
  • Present findings to both technical and non-technical stakeholders.
  • Participate in scientific discussions and strategy meetings, providing AI/ML expertise.
  • Author scientific papers, reports, and patents related to AI/ML in drug discovery.
Desired Qualifications
  • PhD or MSc degree in computational chemistry, computational biology, medicinal chemistry, pharmaceutical sciences, computational sciences, or a related field
  • Fluency in R or Python and Unix (bash)
  • Experience with analyzing and interpreting different omics and/or drug discovery datasets (WES, WGS, bulk/single-cell RNA-Seq, ATAC-Seq, Drug Screening, CRISPR Screening)
  • Experience with using common open-source bioinformatics/NGS workflows (BWA, STAR, Picard, GATK, etc.)
  • Familiarity with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) and developing and deploying machine learning models
  • Highly motivated, strong work ethic, and able to work independently and with a team
  • Excellent skills in communication, organization, and time management
  • For Ph.D. holders: 0-3 years of relevant experience in computational chemistry, AI, and drug discovery
  • Deep expertise with chemical toolkits such as DeepChem, ChemAxon, OEChem, or RDKit
  • Familiarity with drug discovery databases (CTRP, GDSC, PRISM, CCLE, DepMap, LINCS) and publicly available clinicogenomic datasets (CPTAC, TCGA, AACR Project GENIE, etc.)
  • Experience with Cloud Computing Environments (e.g., Azure)
  • Familiarity with relational (e.g., PostgreSQL) and/or NoSQL databases (e.g., MongoDB)
  • Familiarity with container technologies (e.g., Docker)
  • Experience with Generative AI tools and techniques
  • Familiarity with High Performance Computing
  • Familiarity with version control (Git)

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