Scientist, Computational Chemist & MLOps Engineer

Responsibilities

• Develop and apply statistical and machine learning models to experimental datasets
• Design, maintain, and optimize scientific databases and data processing pipelines
• Build reproducible computational workflows for bioinformatics and drug discovery applications
• Analyze high-dimensional biological datasets and communicate findings to multidisciplinary teams
• Contribute production-quality Python code and maintain version-controlled scientific software
• Operate effectively in Linux/HPC environments, including remote systems and cloud-based infrastructure
• Write clear, maintainable, and testable scientific code
• Debug complex computational and data issues independently
• Operate effectively in terminal/Linux-native workflows
• Apply statistically rigorous thinking to experimental interpretation
• Maintain reproducibility and software engineering discipline in research environments
• Partner with scientists across biology, chemistry, bioinformatics, and computational research groups
• Support development of internal computational platforms and modeling infrastructure
• Document workflows, analyses, and software according to reproducible research standards
• Mentor junior team members and contribute to technical best practices

Requirements

• Scientist I: Bachelor’s or Master’s degree in Bioinformatics, Biostatistics, Computational Biology, Computer Science, Data Science, Applied Mathematics, or related field
• Strong programming experience in Python
• Experience working in Linux command-line environments
• Foundational understanding of statistics and machine learning methods
• Experience handling structured and unstructured biological datasets
• Familiarity with source control tools such as Git
• Scientist III (In addition to the above): Advanced degree (PhD preferred) or equivalent industry experience
• Demonstrated history of leading computational projects independently
• Strong background in applied statistics, predictive modeling, and experimental data interpretation
• Experience mentoring scientists or leading technical initiatives
• Ability to translate ambiguous scientific problems into robust computational solutions

Preferred

• Experience with predictive modeling and/or deep learning methods
• Familiarity with modern ML tooling and model evaluation methodologies
• Experience in therapeutic design, RNA biology, genomics, or laboratory experience
• Experience with bioinformatics workflows and sequence-based analysis
• Experience working with large-scale biological or omics datasets
• Experience with distributed computing systems
• Experience operating in collaborative software development environments