Job Abstract

Enzyme engineering is undergoing a paradigm shift thanks to lab automation and AI solutions. We are early adopters and are already reaping benefits of our investments. Now we are ready for the next step and are busy implementing a state-of-the-art, highly automated ML based design pipeline. We are looking for a Protein Engineering Data Scientist to accelerate our efforts and we hope it’s you!

Who we are:

IFF is a global leader in biotechnology; in early 2021, the company consolidated its leadership position by merging with DuPont’s Nutrition & Biosciences division. The IFF laboratories in Palo Alto have a long and dynamic history that goes back to the beginnings of industrial enzyme technology (Genentech/Corning: Genencor, 1982), to becoming the world market leader in sustainable bio-solutions (Danisco).

At IFF Health & Biosciences, our R&D team develops microbial production strains and engineers enzymes for applications in the areas of food & beverages, home & personal care, animal feed, paper & textile processing, health and wellness and more. We have a strong focus on providing sustainable solutions on an industrial scale, and we are constantly evolving and innovating to offer competitive high-end products to our international customers.

Position Description:

• We are looking for an experienced machine learning oriented protein engineer. As part of our team, you will identify the state-of-the-art in data-driven methods for library design, adjust them to our purposes and help implement them for direct use in business projects.
• In our team, machine learning staff, molecular biologists, biochemists, and automation hardware engineers work side-by-side. Improvements are deployed with minimal delay, and you can expect to have large impact on the value of our products.
• As Protein Engineering Data Scientist, you will help to define our design strategy and to implement it so that it seamlessly integrates with our high-throughput cloning and screening workflows. You have freedom to find preferred 3rd party technologies as well as deciding on the components and models that you prefer us to develop internally.

This position will be located at our Palo Alto site working in close collaboration with IFF data engineers, protein, strain, and fermentation scientists on the US East Coast, the Netherlands and Denmark, as well as our network of partners in digital and tech in the Silicon Valley.

• Define our data-driven strategy for enzyme library design that scales to several engineering cycles per month.
• Implement a state-of-the-art, ML driven workflow that enables a high cadence and high-volume design-clone-screen cycle.
• Maintain the platform, monitor and benchmark performance, suggest and implement improvements.
• Scout, engage, and support our partnerships with external companies and service providers in data science that can accelerate and augment our capabilities.
• Document, communicate and teach colleagues best practices.

Required Qualifications:

• PhD or equivalent education and industry work experience in Biological Sciences, Computational Biology, Bioinformatics, Structural Biology, Data Science, or related field.
• Experience with applied enzyme engineering.
• Comprehensive knowledge of deep learning – particularly natural (protein) language modeling, diffusion models and/or genetic algorithms. 
• Expertise in statistical inference.
• Ability to write high-quality, structured, and maintainable code.
• Effective written and oral communication skills and demonstrated ability to work in a team environment.
• Productivity demonstrated through a track record of publications and/or patents.

Bonus Qualifications:

• Experience with modern LIMS, particularly Benchling.
• Experience with analyzing biochemical assays, control of noise, bias and design of experiments.
• Experience with cloud-based ML frameworks (we use AWS Sagemaker) and MLops.
• We emphasize reaching goals through a collaborative effort. It is essential that you can use your good interpersonal and communication skills to achieve results together with others. 
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Compensation ($139,400 - 156,800) is based on a variety of factors including but not limited to work experience, skills, certifications, and location.