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TakedaPharmaceutical Nordics AB · 3 days ago

Research Senior Scientist, AI/ML (Biologics)

Boston, MA

Full-time

Onsite

Mid, Senior Level

$137K/yr - $215K/yr

2+ years exp

Takeda Pharmaceuticals is a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. They are seeking an innovative and dynamic AI/ML Research Senior Scientist to leverage AI/ML in antibody discovery and design, integrating advanced computational methods with experimental strategies to drive breakthroughs in large molecule therapeutics.

Pharmaceuticals

Responsibilities

Develop and implement state-of-the-art AI/ML methodologies for de novo antibody design and discovery, including fine-tuning protein language models and generative protein design

Develop, implement, and deploy advanced machine learning algorithms for the multi-objective optimization of antibodies, antigens, ADCs, and other biologics

Build tools to incorporate data from integrated Design-Predict-Make-Confirm cycles with automated experimental platforms generating quality data at scale needed for project-specific and foundational models

Innovate, develop, and apply predictive models for protein design and developability engineering, utilizing large-scale NGS, in vitro, in vivo and other proprietary in-house and external data sources

Manage and process large-scale biological datasets for model training and evaluation

Stay abreast of advancements in NLP, ML, and generative AI to build novel tools that enhance therapeutic discovery and development

Collaborate with internal experts to optimize the computational discovery infrastructure, offering both individual and team-based innovative solutions

Communicate complex scientific ideas effectively to both technical and non-technical audiences, fostering collaboration across multidisciplinary teams

Qualification

AI/ML methodologiesMachine learning modelsComputational biologyPython programmingCloud computingAntibody designNGS data analysisAnalytical skillsScientific curiosityProblem-solving aptitudeEffective communication

Required

PhD degree in a scientific discipline (or equivalent) with 2+ years relevant experience, or MS with 8+ years relevant experience, or BS with 10+ years relevant experience

Proven track record in developing machine learning models for chemical and biological data, including AI/ML-enabled molecular generation and affinity prediction

Demonstrated experience in modeling antibody/ antigen sequence, structure and interaction

Proficiency in programming languages such as Python and experience with cloud computing capabilities

Strong analytical and problem-solving skills, with demonstrated creativity and the ability to contribute individually and collaboratively

Versatile communicator who can elucidate complex ideas to non-specialists and commitment to continuous improvement and innovation

Demonstrated learning agility, and scientific curiosity while maintaining focus on driving greater impact in the face of uncertainty and change

Strong problem-solving aptitude and strategic thinking with an entrepreneurial mindset

Preferred

Experience developing or applying modern ML architectures for antibody design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)

Experience designing de novo binders for specified targets and epitopes

Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design

Experience with molecular simulation and conformational analysis techniques