Job Description

Are you passionate about harnessing cutting-edge science and data to revolutionize protein engineering? Do you dream of solving some of the world's most complex biological challenges through innovative computational tools and strategies? If so, this is your chance to make a real-world impact.

???? Why This Role Matters

At [Your Company Name], we’re at the forefront of biotechnology, redefining what’s possible in drug discovery, synthetic biology, and molecular design. As a Protein Library Design Data Scientist, you’ll play a critical role in shaping the future of protein engineering. Your work will directly influence the creation of next-generation proteins tailored for life-saving therapies, sustainable materials, and much more.

???? What You’ll Do

In this pivotal role, you will:

• Innovate Protein Library Designs: Develop robust computational pipelines to design diverse, functional protein libraries for applications like therapeutic development, enzyme engineering, and synthetic biology.
• Analyze Big Biological Data: Dive deep into multi-omics datasets (genomics, proteomics, etc.) to uncover patterns and insights, fueling breakthroughs in protein optimization.
• Collaborate with Multidisciplinary Teams: Work alongside computational biologists, wet-lab scientists, and engineers to validate your designs in real-world experiments.
• Push the Frontiers of AI and ML: Leverage machine learning and AI to enhance sequence design, predict protein structures, and optimize functionality.
• Publish and Share Knowledge: Present findings at conferences and contribute to peer-reviewed journals, cementing your role as a thought leader in the field.

????? Your Toolbox

You’re a perfect fit if you bring:

• Education: A PhD (preferred) or MSc in computational biology, bioinformatics, structural biology, or a related field.
• Experience: 2+ years of experience in protein engineering, molecular modeling, or related computational research.
• Technical Skills: Proficiency in Python/R and experience with libraries such as Rosetta, PyMOL, or AlphaFold. Bonus if you’ve dabbled in cloud-based bioinformatics platforms!
• Data Expertise: Hands-on experience with large-scale biological datasets and statistical analysis.
• Problem-Solving: A creative approach to tackling complex biological problems with innovative solutions.