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Salary: USD 178,000 - 215,250 / annual
Eikon Therapeutics is a biopharmaceutical company leveraging revolutionary technology at the intersection of chemistry, engineering, computation, and biology to discover novel treatments for life-threatening diseases. The company's discovery platform is built on groundbreaking innovations from its founders (Nobel Prize, 2014), featuring microscopes that enable real-time, molecular-resolution measurements of protein movement in living cells, unlocking previously intractable protein classes as drug targets.
You will drive diverse early drug discovery efforts across Eikon's target portfolio as a Senior Scientist in Computational Biology. Working in close collaboration with discovery scientists across the organization, you will conceive and evaluate complex, progress-enabling experiments. You will help shape how Eikon evaluates and adopts novel experimental systems and partner with colleagues developing quantitative models to ensure emerging computational methods remain grounded in sound biological reasoning.
Key responsibilities include:
- Drive the development and adoption of novel experimental systems and techniques relevant to Eikon's target portfolio, collaborating closely with experimental and computational teams across programs
- Develop and apply analytical approaches, statistical methods, and proof-of-concept tools to support new experimental strategies
- Analyze multimodal data to evaluate experimental systems, identify enabling insights, and define the limits of interpretation for biological applications
- Apply statistical, biological, and experimental expertise to evaluate complex experiments, distilling information from diverse sources to reach relevant and testable biological conclusions
You are an experienced, rigorous, and results-driven computational scientist with a strong track record driving high-impact contributions to early-stage drug discovery programs. You are an enthusiastic and generous collaborator skilled in both traditional and emergent drug discovery technologies, comfortable working in a fast-paced, cross-functional environment. You are curious, detail-oriented, and a strategic thinker who draws on deep technical and biological knowledge to synthesize data from diverse internal and external sources to resolve nuanced scientific questions. You bring both scientific depth and methodological creativity to emerging experimental systems and work closely with quantitative modeling colleagues to ground new computational approaches in strong biological reasoning.
QUALIFICATIONS:
- Postdoctoral fellowship plus 2 years of relevant experience, OR PhD in bioinformatics, computational biology, or a quantitative field with deep expertise in statistics or method development, plus 5 years of relevant industry experience
- Hands-on experience developing and applying methods for high-dimensional, multi-modal datasets, including experience designing, evaluating, and integrating high-throughput experiments; ability to independently define analytical approaches for novel technologies, develop effective solutions, and validate results with minimal oversight
- Proven experience analyzing complex genomics datasets across one or more technical modalities, such as bulk, single-cell, or spatial transcriptomics, pooled or arrayed CRISPR screens, MAVEs or other multiplexed reporter assays, high content imaging data, Perturb-seq, or other relevant technologies
- Experience working with large consortia and public datasets such as UK Biobank, TCGA, GTEx, ENCODE, gnomAD, and Human Cell Atlas
- Expertise using one or more high-level programming language (e.g., R, Python, or similar) to solve biological problems; well versed in modern coding and computing best practices such as cloud deployment, version control, containerization, etc.
- Demonstrated record of impactful research, evidenced by publications in peer-reviewed journals
- Excellent interpersonal, written, and verbal communication skills
- Additional weight given to qualified applicants with experience partnering with computational/algorithms teams on statistical or machine learning model development and/or methodological development
