Research Scientist, AI​/ML Biologics - Methods Development - Method

Research Scientist, AI/ML Biologics - Methods Development - Method

Join to apply for the Research Scientist, AI/ML Biologics - Methods Development - Method role at Takeda.

This range is provided by Takeda. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

$/yr - $/yr

We are seeking a skilled and motivated Scientist to join our Large Molecule AI/ML team within Computational Sciences. This role focuses on developing and applying machine learning methods to accelerate antibody discovery and optimization on active pipeline projects. You will work closely with protein engineers, computational scientists, and experimental teams to deliver predictive models that directly impact candidate selection and develop ability assessment.

The ideal candidate combines strong ML fundamentals with an interest in biologics and thrives in a fast‑paced, collaborative R&D environment.

• Develop and implement machine learning models for antibody property prediction, including develop ability attributes (stability, aggregation, immunogenicity, viscosity) to support active discovery programs.
• Build predictive tools that rank antibody candidates, flag potential liabilities, and suggest sequence modifications for improved properties.
• Benchmark and evaluate external computational methods and commercial AI platforms; recommend best‑in‑class tools for integration into internal workflows.
• Innovate, develop, and apply predictive models for protein design and develop ability engineering, utilizing large‑scale NGS, in vitro, in vivo and other proprietary in‑house and external data sources.
• Investigate transfer learning and few‑shot learning approaches to enable rapid model deployment on new antibody formats (multi‑specifics, VHH, ADCs) with limited training data.
• Collaborate with experimental teams to validate predictions against assay data, iterate on model development, and integrate AI/ML outputs into Design‑Predict‑Make‑Confirm cycles.
• Establish and maintain AI performance dashboards and KPIs to track prediction accuracy, model reliability, and impact on project timelines.
• Stay current with advances in machine learning for protein science and contribute to internal knowledge sharing.

• PhD in Computational Biology, Bioinformatics, Computer Science, or related field, OR MS with 6+ years relevant experience, OR BS with 10+ years relevant experience.
• Proven track record in developing machine learning models for biological or chemical data.
• Proficiency in Python and machine learning frameworks (PyTorch, Tensor Flow, or scikit‑learn).
• Experience with protein sequence analysis and understanding of antibody structure‑function relationships.
• Strong analytical and problem‑solving skills with demonstrated ability to work both independently and collaboratively.
• Excellent communication skills to convey complex computational concepts to diverse scientific audiences.

• Experience with protein language models (ESM, Prot Trans) or other deep learning architectures for protein property prediction.
• Familiarity with antibody develop ability assessment (stability, aggregation, immunogenicity).
• Experience with transfer learning or active learning approaches.
• Prior experience in pharmaceutical or biotech R&D environment.
• Experience with cloud computing (AWS, GCP) and version‑controlled ML pipelines.

• Ability to lead cross‑functional initiatives and mentor junior scientists.
• Experience in translating computational insights into experimental strategies.
• Strong publication record or demonstrated thought leadership in AI for biology and molecular design.
• Comfort working in fast‑paced, innovation‑driven environments with evolving priorities.

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay practices.

Boston, MA

$ - $

The estimated salary range reflects an…