Senior Quality Control Data Scientist

Regeneron Pharmaceuticals•Rensselaer, IN

1d•$78,700 - $128,700•Onsite

About The Position

Regeneron is building the digital Quality Control laboratory and is looking for a QC Data Scientist to maintain the data layer, validate instrument-to-LIMS data flows, maintain statistical control limits for Review by Exception, and transform QC data into actionable information. This role works across orchestration, LIMS, and instrument software to implement Industry 4.0 laboratories, delivering the QC scientific data framework with strong scientific judgment.

Requirements

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.
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.

Nice To Haves

PhD strongly preferred with 2+ years relevant experience.

Responsibilities

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.