Bioinformatics Analyst III

FULLY REMOTE Pay: $60-$65/hr REQUIRED: Candidate Profile & Must-Haves

• Master’s with strong experience or PhD/fresh PhD acceptable
• Must be very hands-on technically — this role involves heavy coding day-to-day
• Strong proficiency in both R and Python required
• Must have experience working in HPC or cloud environments for parallel computing
• Must have experience processing and analyzing omics data (any type acceptable):
• RNA-seq
• scRNA-seq
• CRISPR screens
• proteomics
• imaging
• other multi-omics datasets
• Ability to extract biological insight from omics data and translate scientific questions into computational analyses
• Strong statistics foundation including regression, modeling concepts, interpretation, and visualization
• Familiarity with AI/ML concepts and LLM-assisted workflows/tools (ChatGPT, Claude, etc.)

What the Manager Seems to Value

• Strong coding ability over purely theoretical knowledge
• Candidates who can independently work with datasets, propose analyses, and troubleshoot workflows
• Practical computational biology/bioinformatics experience with real datasets
• Strong documentation and communication skills:
• clean reporting
• reproducible workflows
• GitHub/README-style organization
• Pharma or translational research experience is helpful, but strong academic backgrounds are still viable

Nice-to-Haves

• CRISPR perturbation analysis
• Drug target ranking/prioritization work
• Spatial or multiplexed omics
• AI/ML model development experience
• Data visualization for scientific/non-technical audiences
• Experience with target valuation or indication strategy work in oncology or related disease areas

The successful candidate will work closely with stakeholders in the Quantitative Insights Lab (QuIL) organization to support cross-project data science efforts spanning in silico perturbation analysis, toxicogenomics, and mechanistic profiling. This work will contribute to the development and evaluation of predictive and comparative frameworks that help rank drug targets, drug combinations, and biological signatures across multiple experimental systems. This will include integrating large-scale omics data to support combination strategy assessment across indications.

Key Responsibilities

• Support perturbation analyses to rank promising drug targets and drug combinations for efficacy prediction.
• Contribute to the development of combination signature estimation approaches and ranking frameworks.
• Ingest, clean, and preprocess multi-modal datasets to enable downstream analysis and modeling.
• Apply AI/ML methods where appropriate, including the use of large language models to accelerate analysis, coding, and workflow development.
• Demonstrate understanding of model development principles, including training, testing, and cross-validation, and help select appropriate models for specific problem types.
• Collaborate with scientific and technical teams to translate biological questions into computational solutions, execute and troubleshoot analyses, and communicate findings clearly and reproducibly.

Qualifications

• MS degree with 5+ years of experience or PhD with 0+ years of experience in a quantitative field such as Bioinformatics, Computer Science, Computational Genetics, Biostatistics, AI/ML, or a related discipline with strong computational training.
• Proficiency in R or Python and standard statistics/ML libraries.
• Experience working in HPC or cloud environments for parallel computing
• Domain knowledge in bioinformatics, computational biology, or related omics-driven data science.
• Strong attention to detail, documentation, and communication skills.
• Ability to independently execute ideas and research solutions within project scope.

Required Technical Skills 1. Experience processing and analyzing RNA-seq, imaging data, CRISPR screens, or other similar NGS/genomic data. 2. Proficient in statistical and programming languages such as R and Python, with ability to perform parallel computing using HPC 3. Experience writing custom functions in R or Python to statistically interrogate and visualize omics data. 4. Demonstrated ability to execute custom computational analysis plans leveraging Client algorithms, relevant databases, and AI/ML approaches where appropriate. 5. Familiarity with machine learning fundamentals, including model selection, training, testing, and cross-validation. 6. Experience using LLM-based tools or coding assistants to support analysis, coding, and workflow development.