Key Responsibilities

Technical Leadership

• 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.

Product & Business Impact

• 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.

Data & Platform Excellence

• 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.

Mentorship & Influence

• 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.

Required Qualifications

• 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
  • Design and implementation of Retrieval-Augmented Generation (RAG) architectures
  • Agent orchestration frameworks and multi-step tool-using agents
  • 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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