MSAT Lifecycle Management Data & Process Specialist (Contract)

Atrium Works•Boston, MA

4d•Hybrid

About The Position

MSAT Lifecycle Management Data & Process Specialist executes the completeness, validation, and delivery of commercial manufacturing data for the Manufacturing Science and Technology (MSAT) department throughout the commercial product lifecycle. Operating within the Lifecycle Management (LCM) group, this role serves as the functional oversight for department data from CASGEVY manufacturing. The Specialist coordinates, verifies, and distributes global data to support cross-functional studies, regulatory agency responses, and technical investigations within the Commercial Manufacturing & Supply Chain network. This position bridges manual data management with advanced analytics, partnering alongside Technical Operations and leveraging generative AI technologies to automate internal workflows, ensure data integrity, and support product commercialization activities.

Requirements

Bechlor's degree in Life Sciences (or engineering with life sciences concentration) with a minimum of 0-2 year of relevant experience, or an equivalent combination of education and experience.
Direct experience managing, preparing, and verifying data for technical regulatory/agency reports and questions.
Demonstrated hands-on experience utilizing generative AI technologies to streamline or automate internal processes.
Strong background working with data management software configurations spanning data collection, preparation, storage, analysis, and visualization.
Basic knowledge of programming or coding using Python, SQL, C++, or a similar programming language.
General understanding of statistical concepts and experience interpreting or analyzing data.
Strong technical background in therapeutic pharmaceutical manufacturing processes, with a preference for Cell & Gene Therapy (CGT) cGMP environments.
Thorough knowledge of current Good Manufacturing Practices (cGMP) and regulatory requirements related to commercial product lifecycle stages.
Strong problem-solving and critical thinking skills with the ability to analyze complex datasets and make sound, data-driven recommendations.
Excellent written and verbal communication skills, with a proven ability to translate complex technical information for diverse stakeholders and cross-functional teams.
Strong collaborative and leadership skills to contribute to and provide technical data guidance within project frameworks.

Responsibilities

Enter and approve manufacturing data from global internal and external manufacturing networks into internal validated databases.
Provide validated data packages and manufacturing process content for regulatory submissions and responses, including data verification support.
Field, prioritize, and execute routine and ad-hoc requests to provide datasets for various departmental and cross-functional projects.
Support Continuous Process Verification (CPV) deliverables and Annual Product Quality Review (APQR) reporting by managing global site data entry, assisting with authoring reports, and managing completeness of historical data.
Execute and sign off on Data Verification (DV) protocols for various regulatory submissions and information requests.
Ensure all data handling complies with cell and gene therapy (CGT) requirements, current Good Manufacturing Practices (cGMP), data integrity guidelines, and company internal controls.
Analyze complex manufacturing datasets to identify Out-of-Trend (OOT) and Out-of-Specification (OOS) anomalies, proactively flagging processing issues.
Partner with other MSAT groups by assisting with trend/process investigations and CPV-related quality events.
Participate in cross-functional manufacturing data review forums to evaluate proactive process performance, monitor key metrics, and mitigate operational risks through early anomaly detection.
Synthesize technical data, resolve data issues independently, and generate metrics/data-driven reports to support compliance, efficiency projects, and executive decision-making.
Provide departmental and cross-functional training on data systems/practices.
Utilize generative AI technologies and partner with Automation, DTE, and QA teams to test, integrate, and implement digital tools, such as electronic Batch Record (eBR) code readers and AI-driven data automation.
Evaluate emerging data management software (spanning collection, preparation, storage, analysis, distribution, and visualization) to improve department-wide workflow efficiency and reduce manual errors.
Cross-train team members on automated tools and standardized data verification practices to build department-level resilience.