Senior Quality Control Data Scientist
Regeneron
- Location
- RENSSELAER
- Work model
- On-Site
- Level
- Senior
- Posted
- Sep 10, 2026
Skills
About this role
Build our future together: Regeneron is building the digital Quality Control laboratory, and looking for a QC Data Scientist to keep that data layer running: validating that what reaches LIMS is what left the instrument, maintaining the statistical control limits behind Review by Exception, and turning QC data into information leaders can act on. Working across the orchestration, LIMS, and instrument software, helping to bring Industry 4.0 laboratories into practice, this role delivers the QC scientific data framework day to day, with strong scientific judgment. When & where: Monday–Friday, 8:00am–4:30pm Location: Rensselaer, New York, United States Discover your role: Execute daily validation and reconciliation of instrument-to-LIMS data flows across the lab bus confirming the completeness, accuracy, and integrity of data transferred from instruments (Empower and others) into LIMS and data lake solutions. Maintain and monitor statistically valid control limits and Review by Exception (RBE) logic; triage flags and out-of-trend signals with method-specific scientific judgment and escalate issues. Validate parsing and data-transformation configurations for every new method onboarding, instrument qualification, and software release ensuring the pipeline interprets analytical outputs correctly before they reach LabWare. Build and maintain dashboards, reports, and analytics that translate QC data into actionable information for analysts, managers, and stakeholders. Apply statistical and machine-learning-supported tools (multivariate analysis, trending, anomaly detection) to support performance-monitoring programs and technical investigations. Troubleshoot data-transfer discrepancies at the scientific level distinguishing formatting and rounding artifacts from true signal-integrity issues and driving them to resolution. Support GxP data integrity (ALCOA+) for the scientific data layer; contribute to investigations, CAPAs, and inspection readiness. Partner with QC analysts, laboratory informatics / IT, and OT to keep the scientific data pipeline healthy leading the scientific and data-quality dimension while infrastructure and platform administration remain with IT / OT. Use LLMs (Claude, ChatGPT, etc.) to accelerate drafting, coding, and analysis with the judgment to keep probabilistic AI out of validated GxP data and quality decisions. This role requires: BS/BA Biochemistry, Chemistry, Biology, Data / Computational Science, or a related field; with 6+ years relevant analytical or QC data experience — preferably in the pharmaceutical or biotechnology industries. PhD strongly preferred with 2+ years relevant experience. Proven understanding of analytical and bioassay variability and the statistical basis for control limits, trending, and Review by Exception. Applied statistics and data analysis: multivariate methods (PCA / PLS), trending, and anomaly detection; exposure to machine-learning tools. Strong SQL and data-modeling skills (ETL, data cleansing, transformation) and programming for data manipulation (Python and/or R). Specialized statistical / modeling software (e.g., JMP, Minitab, SIMCA, Discoverant, MATLAB). Experience with Tableau, Spotfire, and Power BI. Does this sound like you? Apply now to take your first step towards living the Regeneron Way! We are committed to building a workplace with an inclusive culture. Regeneron is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion or belief (or lack thereof), sex, sexual orientation, gender identity or expression, gender reassignment, marital or civil partnership status, civil status, pregnancy or parental status, age, disability, nationality, citizenship status, ethnic or national origin, membership of the Traveler community, familial status, genetic information, military or veteran status, or any other characteristic protected under applicable law. Where required, we