Scientist, Data - Analytical & Biological Mass Spectrometry (ABMS)

Role Summary: 

Regeneron's Data Enablement and Analytics (DEA) team within the Analytical & Biological Mass Spectrometry (ABMS) group is seeking a Data Scientist to design, develop, and maintain scalable data platforms and software solutions that enable automated, reliable Liquid Chromatography-Mass Spectrometry (LC-MS) analytical data pipelines — from instrument-generated raw data to decision-ready insights. This role operates at the intersection of data engineering, software development, and LCMS–based analytics, supporting biotherapeutic drug development programs across diverse modalities and stages of development. 

Your work will enable scientists and leadership to more efficiently access, analyze, visualize, and act on ABMS data, increasing organizational capacity, reliability, and operational efficiency to help advance Regeneron’s preclinical and clinical programs. 

As a core contributor to ABMS data solutions, you will collaborate closely with DEA team members, ABMS laboratory scientists and managers, IT solution partners, and other DEA teams across the Product Analytics & Process Development (PAPD) organization. The ideal candidate is comfortable operating in an evolving, greenfield environment. 

Key Responsibilities: 

• Design, develop, and maintain scalable, automated data pipelines that integrate LC-MS analytical workflows from raw data acquisition through cloud storage, processing, structured archival, and visualization. 

• Design, build, and optimize ETL/ELT workflows, data integrations, and APIs to enable seamless interoperability across heterogeneous systems (e.g., LC-MS instruments, LIMS, SDMS, data processing software, and enterprise data stores). 

• Partner with IT and DEA teams to design, deploy, and manage data infrastructure (e.g., data lakes, data warehouses) for robust data ingestion, processing, and storage. 

• Drive platform reliability and performance through proactive monitoring, observability practices, and continuous improvement. 

• Contribute to ABMS data strategy, architecture, and governance by establishing standards for data quality, code quality, compliance, accessibility, and platform reliability; support adoption through documentation, training, and bestpractice guidance. 

• Stay current with emerging technologies and evaluate innovative approaches in data engineering, scientific informatics, and operational analytics.

Required Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Software Engineering, Data Science, Bioinformatics, Computational Biology, Computer Engineering, Information Systems, or a related quantitative discipline. 

• 0–5 years of hands-on experience in data engineering, scientific data infrastructure, or a closely related technical role. 

• Proficiency in Python and SQL; demonstrated ability to write production-quality code for data pipelines, data/file parsing, and automated workflows, apply software engineering best-practices such as version control, testing, documentation, and code review. 

• Experience developing APIs, ETL/ELT pipelines, or data access layers to connect backend systems with analytical and visualization tools. 

• Experience with relational database design and building structured data stores from semi-structured or unstructured scientific data. 

• Experience with cloud platforms (AWS preferred), including data storage and compute services. 

• Understanding of data validation, logging, and error‑handling concepts in production data pipelines. 

• Strong communication skills, with the ability to translate scientific data requirements into welldocumented, maintainable technical solutions and collaborate effectively across scientific, analytical, and IT teams. 

Preferred Qualifications: 

• Prior experience in biopharmaceutical, biotech, or life sciences industry, particularly within an analytical laboratory environment. 

• Familiarity with mass spectrometry raw data and processed data formats. 

• Familiarity with LC-MS software ecosystems (e.g., Skyline, LabKey Panorama, Protein Metrics Byosphere, Genedata Expressionist, or Waters UNIFI/Empower). 

• Familiarity with Laborat