Senior Data Scientist - Protein Data Pipelines

Career Category Research Job Description Senior Data Scientist - Protein Data Pipelines Role Summary The Senior Data Scientist - Protein Data Pipelines will play a critical role in enabling predictive modeling for protein sequence, structure, and function by building scalable, reliable, and reproducible data pipelines. This role will focus on transforming protein property data and related scientific outputs into ML-amenable assets that support model training, inference, deployment, and ongoing use across research programs. Working at the intersection of data engineering, MLOps, computational biology, and applied machine learning, this individual will partner with ML developers, wet-lab scientists, domain experts, and distributed technical teams to translate scientific and engineering needs into robust data and inference solutions. The successful candidate will develop reusable frameworks for data engineering, model inference, deployment, validation, testing, and monitoring across in-house and external machine learning models. This role is ideal for someone who enjoys building production-ready scientific data systems, collaborating across disciplines, and converting complex domain needs into maintainable technical solutions that scale across discovery pipelines. Key Responsibilities Scalable Data Pipelines for model training Design and maintain scalable data pipelines that support predictive model training, with emphasis on protein sequence or structure-to-function applications. Build ML-model amenable data assets for protein property data that are readable, quality-controlled, reproducible, and suitable for reuse across programs. Translate scientific and engineering needs into reliable data solutions that support ongoing research and model-development workflows. Model Deployment & Inference Pipelines Develop deployment strategies and pipelines to embed trained models into ongoing projects. Develop reusable inference, deployment, and testing frameworks for in-house and external machine learning models. Convert model-development outputs into maintainable technical solutions that can be used reliably by research teams. Data Quality, Validation & Reproducibility Establish data quality, validation, monitoring, and reproducibility practices for protein property and related scientific datasets. Implement validation and monitoring approaches that improve confidence in downstream model training, inference, and deployment. Document data lineage, assumptions, validation outcomes, and reproducibility practices to support long-term reuse. Cross-Functional Collaboration & Technical Coordination Serve as a liaison between machine-learning developers and domain experts, including wet-lab collaborators where applicable. Own and mediate collaborations between ML developers and wet-lab teams to ensure that data, modeling, and experimental needs are aligned.