Bachelor's or Master's degree in a relevant technical field.
10+ years of experience in machine learning and AI governance.
Strong understanding of neural networks and machine learning algorithms.
Experience with model governance and risk management programs.
Proficient in model validation methodologies and performance measurement.
Hands-on experience with Java, Python, SQL, and machine learning frameworks.
Strong analytical skills and ability to communicate complex concepts.
Responsibilities
Design and maintain neural network models and governance frameworks.
Lead model validation activities, including performance and bias testing.
Establish and monitor model lifecycle processes from development to retirement.
Develop governance metrics and dashboards for model performance visibility.
Collaborate with cross-functional teams to embed governance in model development.
Create and maintain comprehensive model documentation and validation reports.
Investigate model performance issues and recommend corrective actions.
Benefits
Country-specific benefits tailored to employee well-being.
Annual incentive bonus eligibility.
Full Job Description
Basic Functions: This position provides technical leadership for building neural network models and governance oversight for machine learning and linking solutions across the enterprise. The engineer develops neural network model governance frameworks, builds explainability capabilities, leads model reviews and validations, collaborates with engineering and data science teams, and ensures compliance with enterprise AI policies and industry standards. The position serves as a senior subject matter expert on linking models, explainability, monitoring, performance assessment, and governance. Accountabilities
Design, implement, and maintain neural network models and enterprise-wide model governance frameworks, standards, and controls for machine learning and linking solutions.
Lead model validation and review activities, including performance assessment, precision analysis, explainability evaluation, bias testing, and risk identification.
Establish and monitor model lifecycle processes covering development, testing, deployment, monitoring, retraining, and retirement.
Develop governance metrics, dashboards, and reporting to provide visibility into model performance, drift, risk exposure, and business impact.
Partner with data scientists, software engineers, architects, and business stakeholders to ensure governance requirements are embedded throughout the model development lifecycle.
Create and maintain model documentation, validation reports, audit artifacts, governance approvals, and regulatory evidence.
Evaluate model performance using statistical analysis, precision and recall measurements, confidence scoring, explainability techniques, and business outcome monitoring.
Lead investigations into model degradation, unexpected behavior, accuracy declines, and governance exceptions, recommending mitigation strategies and corrective actions.
Establish controls and monitoring processes to identify data drift, concept drift, and operational risks affecting model quality.
Communicate governance findings, model risks, validation outcomes, and recommendations to technical teams, executive leadership, auditors, and business stakeholders.
Drive continuous improvement of model governance processes, tools, standards, and best practices across the organization.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Machine Learning, Engineering, or a related technical discipline.
10+ years of experience in machine learning, data science, analytics, model development, model validation, or AI governance.
Strong understanding of neural network models, machine learning algorithms, statistical modeling techniques, predictive analytics, and AI systems.
Experience establishing or operating model governance, model risk management, AI governance, or Responsible AI programs.
Strong knowledge of model validation methodologies, performance measurement, drift detection, bias assessment, and explainability techniques.
Experience with cloud-based platforms including Azure, Databricks, Spark, Cosmos DB, and large-scale data processing technologies.
Hands-on experience with Java, Python, SQL, machine learning frameworks, and data analysis tools.
Experience developing dashboards and automated monitoring solutions for model performance and governance metrics.
Strong analytical and problem-solving skills with the ability to evaluate complex technical and business risks.
Strong communication and presentation skills, with the ability to explain complex model behavior and governance findings to both technical and non-technical audiences.
Ability to influence technical direction, governance strategy, and enterprise decision-making across multiple organizations.
U.S. National Base Pay Range: $95,300 - $158,800. Geographic differentials may apply in some locations to better reflect local market rates.This job is eligible for an annual incentive bonus. We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
About Relx Group
RELX Group is a global provider of information-based analytics and decision tools for professional and business customers. The company operates in four market segments: scientific, technical and medical; risk and business analytics; legal; and exhibitions. RELX's products and services include electronic databases, online information services, workflow tools, and print and digital books. The company was founded in 1993 and is headquartered in London, England.
