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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in Computational Biology and Machine Learning, with a strong focus on Genomics and the application of ML methods to biological data. Proven ability to lead teams, mentor colleagues, and translate complex scientific concepts into actionable insights for diverse stakeholders.
Highest-signal resume keywords
PhD In Computational BiologyMachine Learning ExpertiseGenomics ApplicationLeadership ExperienceExcellent Communication Skills
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning MethodsTransformersVariational AutoencodersDiffusion ModelsClassical Machine LearningApplied Use CasesModel Evaluation
Soft Skills
MentoringCollaborationAdaptability
Tools & Technologies
PyTorchML Frameworks
Industry Keywords
Drug DevelopmentTranslational ResearchBiological DataCustomer-Relevant Deliverables
Tech Stack
Tools & technologiesPyTorch
About the role
Key responsibilities & impact- Provide scientific leadership across model evaluation and applied use cases
- Translate foundation models into impactful, real-world discovery workflows
- Ensure work addresses relevant challenges in drug development and translational research
- Serve as a key scientific partner to the research team
- Mentor and support junior colleagues
- Collaborate with Product and Business Development
- Turn cutting-edge biology and machine learning into customer-relevant deliverables
- Contribute leadership and strategy as the team scales
Requirements
What you’ll need- PhD in Computational Biology, Machine Learning, Bioinformatics, or a related field, with a strong focus on Genomics + ML, and 2+ years of industry (non-academic) experience OR MSc in a relevant field and 5+ years of industry experience applying ML to Genomics problems
- Strong understanding of ML/AI methods for biological data, including transformers, VAEs, diffusion models, and classical ML
- Hands-on experience with modern ML frameworks such as PyTorch or equivalent
- Leadership experience with small teams working toward defined roadmaps or projects
- Excellent communication skills, with ability to translate complex science into clear, actionable insights for technical and non-technical stakeholders
- Comfort working in a fast-paced, high-iteration environment, moving quickly from prototype to experiment to insight
- Strong passion for building at the intersection of biology and machine learning