Lead end-to-end development of advanced AI models (e.g.,LLM, NLP, classification, regression, deep learning).
Architect scalable model pipelines and data workflows in cloud-based environments.
Establish best practices in model validation, explainability, fairness, and governance.
Conduct rigorous experimentation, A/B testing, and performance monitoring.
Drive research into emerging AI techniques and evaluate applicability to LexisNexis products.
Partner with Product, Engineering, and Business stakeholders to translate requirements into analytical solutions.
Identify opportunities to enhance risk scoring, entity resolution, legal analytics, fraud detection, compliance monitoring, or related product areas.
Present insights and recommendations to senior leadership and non-technical audiences.
Ensure models meet regulatory, compliance, and ethical AI standards.
Work with structured and unstructured data, including legal texts, transactional data, and graph-based datasets.
Collaborate on data engineering strategies to ensure high-quality, scalable datasets.
Optimize model performance for production deployment.
Implement monitoring frameworks to ensure model robustness and stability.
Mentor and coach junior and mid-level data scientists.
Lead code reviews and promote reproducible research practices.
Contribute to strategic roadmap planning for data science initiatives.
Act as a subject matter expert in advanced analytics within the organization.
Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related quantitative field.
8+ years of progressive experience in data science, applied machine learning, or AI engineering roles, with demonstrated ownership of production-grade systems.
3+ years of hands-on experience designing and deploying LLM-based systems or advanced NLP solutions within enterprise-scale products.
Strong programming proficiency in Python and deep experience with modern ML/NLP frameworks and tooling.
Demonstrated technical expertise in:
Deep understanding of LLM capabilities, limitations, and mitigation strategies across commercial (e.g., OpenAI, Anthropic) and open-source models
Prompt engineering, systematic prompt evaluation, and optimization methodologies
Embedding models, vector databases, and semantic retrieval techniques
Strong understanding of model evaluation methodologies
Proven experience deploying, monitoring, and optimizing AI systems in cloud environments (AWS, Azure, or GCP).
Strong written and verbal communication skills in English, with the ability to effectively collaborate across global teams.
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