Evozyne designs and builds engineered protein therapeutics using our AI-native platform, transforming what’s possible in immune-mediated disease treatment. Our Data Scientists partner closely with our research teams to transform complex experimental data into insights that guide molecule design and program strategy, turning bold ideas into therapies that can meaningfully improve patients’ lives. This posting helps us connect with candidates for future opportunities. While this listing is not tied to a current opening, we encourage you to join our talent community so we can connect with you when new roles emerge!

Key Responsibilities

• Analyze and integrate diverse experimental datasets (e.g., sequencing, biophysical, cell-based, and functional assays) to inform therapeutic design decisions
• Develop computational and statistical models to support protein engineering, optimization, and candidate selection
• Design and implement workflows for data processing, visualization, and interpretation that enable rapid, high-quality decision-making
• Partner with discovery scientists to translate biological questions into quantitative analyses and predictive approaches
• Apply machine learning and modeling methods to improve design strategies
• Build scalable tools and pipelines that enhance reproducibility and accessibility of experimental insights
• Communicate findings clearly to cross-functional teams and contribute to project strategy and prioritization

Who You Are

You thrive in an early-stage start-up environment and are motivated by the opportunity to build new therapies that can transform patients’ lives. You combine agility with scientific rigor to deliver high-quality results, and you bring natural curiosity and a collaborative mindset to solving complex challenges.

Minimum Qualifications

• Undergraduate and/or graduate level education focused on data science, computational biology, bioinformatics, computer science, machine learning, AI, or a similar field
• Hands on experience analyzing experimental, biological, chemistry, or physics datasets (industry, startup, or academic lab)
• Ability to understand experimental context (read protocols, interpret assay outputs) and partner effectively with experimentalists
• Solid grasp of EDA and basic statistics (distributions, confidence intervals, hypothesis testing)