Senior Data Scientist III
At a glance
Senior Data Science role at RELX. Shanghai_Tianshan · full-time.
Pay not stated Data Scientist salary
dataroles summary, based on the employer's posting.
What you'll do
- Build AI models from initial design through release, including language, classification, regression and deep-learning systems
- Design cloud-based data and model workflows, then set standards for validation, explainability, fairness and governance
- Run experiments and A/B tests, monitor results, and assess emerging AI methods for product use
- Turn product and business needs into analytical solutions for areas such as risk scoring, legal analytics and fraud detection
- Present recommendations to senior and non-technical audiences, guide junior scientists, and lead code reviews
What you bring
- A master’s or doctorate in computer science, AI, machine learning, NLP or another related quantitative field
- At least eight years in data science, applied ML or AI engineering, including ownership of production systems
- Three or more years building and deploying LLM systems or advanced NLP solutions in enterprise products
- Strong Python skills and hands-on experience with RAG, agent frameworks, prompt evaluation, embeddings and semantic retrieval
- Experience deploying and optimizing cloud AI systems, plus strong English communication for global collaboration
Who this fits
This senior role suits someone who can lead production AI work while mentoring other data scientists and shaping technical direction. You’ll collaborate with product, engineering and business partners, and communicate with global teams in English. The position is based in Shanghai, China.
From the employer
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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