Manager Data Science

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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