Risk Consulting - Regulatory Compliance - Life Sciences - Manager

EY•Dallas, TX

8d•$114,800 - $239,300•Hybrid

About The Position

EY’s Life Sciences Risk Consulting Services team advises organizations in highly regulated environments where validated systems, reliable data, regulatory compliance, business performance, and patient safety are critical. The Manager leads client engagements and workstreams focused on the compliant development, implementation, and lifecycle management of artificial intelligence and machine learning solutions used in GxP-regulated processes and systems. The role establishes and operationalizes risk-based validation and assurance strategies for AI/ML solutions that may affect patient safety, product quality, data integrity, or regulatory decisions. It includes defining intended use and assurance requirements; assessing data and model performance; testing AI-enabled workflows; establishing lifecycle controls; and determining appropriate assurance when models, data, infrastructure, or business processes change. The Manager works with client Quality, Regulatory, IT, Data Science, Engineering, Security, Privacy, and business stakeholders across manufacturing, laboratory, clinical, pharmacovigilance, quality, and regulatory operations, while supporting audit and inspection readiness. The Manager oversees engagement delivery, workstream management, team coordination, deliverable quality, engagement risks and dependencies, and communication of findings and recommendations. The role also coaches and develops junior professionals, contributes to client relationships, and supports the development of AI/ML validation and assurance capabilities within EY’s Life Sciences practice. The position uses a hybrid model, with client-serving personnel expected to work together in person approximately 40% to 60% over the course of an engagement, project, or year, and requires willingness to travel.

Requirements

Bachelor’s degree in data science, computer science, engineering, statistics, life sciences, pharmacy, or a related discipline.
At least six years of relevant professional experience in computerized-system validation, quality assurance, software testing, data science, machine learning, technology risk, or a related field.
Meaningful experience leading projects or workstreams in a regulated life sciences environment, including GxP business processes, computerized systems, validation, risk management, or compliance programs.
Demonstrated experience developing and applying risk-based validation or computerized software assurance approaches and reviewing validation and testing documentation for quality and regulatory adequacy.
Practical knowledge of AI/ML development, testing, performance evaluation, lifecycle controls, and associated model risks.
Experience using Python, SQL, R, or comparable analytical tools, or critically reviewing and overseeing technical analyses produced with these tools.
Ability and willingness to work in a team-led and leader-enabled hybrid model, including the stated in-person collaboration and travel expectations.
Deep grounding in GxP computerized-system validation, computerized software assurance, software development lifecycle controls, and Agile delivery models.
Strong understanding of machine learning development and evaluation, including data preparation, feature engineering, training, validation, testing, performance measures, and model limitations.
Demonstrated ability to evaluate AI/ML reliability, robustness, explainability, bias, data or concept drift, and appropriate human oversight.
Strong understanding of data provenance, lineage, quality, integrity, privacy, security, and fitness for intended use.
Ability to critically evaluate validation strategies, requirements, risk assessments, traceability, test design, technical evidence, deviations, and validation conclusions.
Ability to analyze complex data and test results and challenge technical evidence appropriately.
Ability to translate evolving regulatory, Quality, and technical expectations into pragmatic and scalable validation and assurance approaches.
Strong engagement management and team leadership capabilities, including prioritizing work, directing multidisciplinary teams, managing risks and dependencies, and maintaining quality across concurrent activities.
Strong written, verbal, and executive-level communication skills, including explaining technical findings, model risk, and recommendations to Quality, business, technology, and regulatory stakeholders.
Ability to develop junior professionals and foster an innovative, inclusive, and team-oriented work environment.

Nice To Haves

Master’s degree in data science, computer science, engineering, statistics, life sciences, pharmacy, or a related discipline.
Experience applying GAMP 5, FDA Computer Software Assurance principles, 21 CFR Part 11, EU Annex 11, data-integrity principles, and quality risk management under ICH Q9 and ICH Q10.
Experience with validated platforms or processes such as manufacturing execution systems, laboratory information management systems, quality management systems, clinical systems, pharmacovigilance systems, regulatory content platforms, enterprise resource planning systems, Veeva Vault applications, or validation lifecycle management systems.
Familiarity with MLOps or LLMOps, model registries, version control, automated testing, cloud AI services, and controlled deployment pipelines.
Experience with AI/ML applications such as automated visual inspection, predictive maintenance, process monitoring, quality-event analytics, clinical or safety analytics, document intelligence, or generative AI assistants.
Experience managing multidisciplinary teams or client workstreams involving Quality, business, data science, engineering, and technology stakeholders.
Experience supporting health-authority inspections, internal audits, supplier audits, or remediation programs involving computerized systems, AI-enabled solutions, or data integrity.

Responsibilities

Lead client engagement teams and workstreams delivering AI/ML validation and assurance projects in the life sciences domain.
Oversee assessments of GxP applicability, system criticality, and risk for AI/ML use cases, considering patient safety, product quality, data integrity, and regulated decisions.
Guide clients in defining intended use and context of use, regulated decisions supported, human oversight, foreseeable failure modes, limitations, and prohibited uses.
Define validation and assurance strategies and oversee development, execution, and quality review of validation plans, requirements, risk assessments, traceability matrices, test protocols and evidence, deviations, and validation summary reports.
Guide teams in establishing scientifically and risk-appropriate acceptance criteria for data quality and model performance, including appropriate performance measures.
Oversee evaluation of data provenance, representativeness, completeness, labeling quality, lineage, transformation logic, training/test-data segregation, leakage risk, and data-integrity expectations.
Lead the design and review of functional, integration, regression, negative, boundary, security-role, audit-trail, data-reconciliation, and user-acceptance testing strategies for AI-enabled systems and workflows.
Guide reliability assessments including reproducibility, robustness, bias, subgroup performance, explainability, stress, and edge-case testing; ensure limitations and residual risks are documented, communicated, and addressed.
Advise on production monitoring for model performance, data and concept drift, anomalous outputs, overrides, human review, complaints, incidents, and signals that may trigger investigation or revalidation.
Establish governance and change-assessment approaches for model updates, retraining, prompt or knowledge-base changes, infrastructure changes, and vendor releases.
Oversee supplier assessments for external models, cloud platforms, generative AI services, and software components; evaluate evidence gaps and advise on proportionate compensating controls.
Lead generative AI and large language model assurance covering prompt and configuration controls, grounding and retrieval quality, output variability, inaccurate or unsupported outputs, sensitive-data handling, guardrails, and human verification.
Advise clients on deviations, investigations, corrective and preventive actions, periodic reviews, inspection readiness, and responses to Quality or regulatory observations involving AI-enabled systems.
Manage day-to-day engagements and workstreams, deliverable quality, team activities, engagement risks and dependencies, and communication of findings and recommendations to client stakeholders.
Coach and develop junior professionals, provide technical review and direction, contribute to client relationships, and support development of Life Sciences AI/ML validation and assurance capabilities.
Work in a team-led, leader-enabled hybrid model and travel as needed; client-serving individuals are expected to work together in person approximately 40% to 60% over an engagement, project, or year.