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# Senior Software Engineer II

- **Company:** LexisNexis Risk Solutions Group
- **Location:** Boca Raton, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Data Analysis, Artificial Neural Networks, Microsoft Azure, Big Data, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Cloud Platform System, Apache Spark, Model Validation, Information Technology, Cosmos DB, Drift Detection, Databricks
- **Published:** November 11, 2026
- **Apply:** https://relx.wd3.myworkdayjobs.com/RiskSolutions/job/Boca-Raton-FL/Senior-Software-Engineer-II_R119924

## About the Role

* 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.

## 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., * 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.