Senior Data Scientist III
Job Description
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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