Manager Data Science
At a glance
Manager-level Data Science role at RELX. Shanghai_Tianshan · full-time.
Pay not stated Data Science salary
dataroles summary, based on the employer's posting.
What you'll do
- Build AI models from initial design through deployment, including LLM, NLP, classification, regression and deep learning systems
- Design cloud-based model pipelines, then test and track their performance through experiments, A/B tests and monitoring
- Work with product, engineering and business partners to turn needs into solutions for risk scoring, legal analytics and related areas
- Prepare model findings for senior leaders and nontechnical audiences, while meeting regulatory and responsible-AI standards
- Coach data scientists, review code, support reproducible research and help shape data science plans
What you bring
- A master’s or PhD in computer science, AI, machine learning, NLP or another listed quantitative field
- At least eight years in data science, applied machine learning or AI engineering, including ownership of production systems
- At least three years building and deploying LLM systems or advanced NLP in enterprise products
- Strong Python skills, hands-on cloud AI deployment experience and expertise in LLM, RAG, agents, retrieval and model evaluation
- Professional English communication skills for working with teams across regions
Who this fits
This manager role suits an experienced technical data scientist who can own production AI systems and guide junior and mid-level colleagues. The position is based in Shanghai and involves collaboration across global teams.
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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