CALIBRE Systems, Inc. is an employee-owned, mission-focused solutions and digital transformation company. We are looking for a highly motivated Privacy‑Preserving Record Linkage Automation (PPRLA) Program Manager to join our dynamic team supporting a federal client. This position is accountable for end-to-end delivery of the PPRLA task, providing strategic leadership, technical oversight, and federal compliance assurance to ensure development, validation, and operationalization of a secure, automated record linkage solution.

Responsibilities include, but are not limited to:

· Maintain full accountability for successful delivery of all PPRLA objectives, deliverables, and outcomes.

· Provide strategic oversight for the design, development, testing, validation, and enhancement of the automated PPRLA solution.

· Oversee integration of probabilistic and deterministic linking strategies, dynamic confidence scoring, machine learning models, and explainable AI components.

· Ensure that linkage methodologies meet privacy, security, and compliance standards while maintaining analytical utility.

· Oversee execution of environmental scans, vendor/software assessments, and technology acquisition activities.

· Ensure proper management of encryption keys, third party Key Escrow services, secure data transfer, and destruction of keys following linkage.

· Review and approve linkage performance metrics, quality assessment results, and reporting outputs.

· Ensure quality assurance processes – both ML based and manual – are effectively implemented and maintained.

· Guide preparation and dissemination of required documentation, white papers, dashboards, and reporting artifacts.

· Maintain clear, professional communication with the Contracting Officer’s Representative (COR), National Institute of Health (NIH) stakeholders, and project leadership.

· Oversee risk management, quality assurance, and federal data security practices related to PPRLA activities.

Required Skills

• Demonstrable, in-depth knowledge of probabilistic and deterministic record linkage methodologies, including POI and PPRL approaches
• Strong statistical and analytical expertise, including evaluation of linkage accuracy using precision, recall, and confidence scoring
• Knowledge of artificial intelligence and machine learning methods applied to data linkage
• Strong communication skills with the ability to clearly convey technical concepts to both technical teams and federal stakeholders

Preferred Skills

• Familiarity with differential privacy and anonymization techniques for sensitive data
• Knowledge of explainable AI (XAI) or algorithm transparency approaches

Required Experience

• Bachelor’s degree in Data Science, Statistics, Behavioral Science, or related field
• 5+ years of technical experience in data linkage, automation, and/or secure data environments
• 2+ years of experience in risk management, quality assurance, and maintaining data security for federal datasets
• 2+ years of experience serving as a Task Lead on projects of comparable size and complexity
• Relevant professional certifications commensurate with duties (e.g., PMP, CISSP, CISM, SANS GSNA)

Preferred Experience

• Experience managing AI-enabled or self-learning systems in production environments
• Experience supporting secure research enclaves or multi-agency data linkage initiatives
• Experience overseeing or implementing explainable AI (XAI) solutions

• Demonstrable, in-depth knowledge of probabilistic and deterministic record linkage methodologies, including POI and PPRL approaches
• Strong statistical and analytical expertise, including evaluation of linkage accuracy using precision, recall, and confidence scoring
• Knowledge of artificial intelligence and machine learning methods applied to data linkage
• Strong communication skills with the ability to clearly convey technical concepts to both technical teams and federal stakeholders

Preferred Skills

• Familiarity with differential privacy and anonymization techniques for sensitive data
• Knowledge of explainable AI (XAI) or algorithm transparency approaches