Manager Data Science

AI in this role

openaianthropic

prompt-engineeringragai-evaluationnlp

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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How we rate this

Manager Data Science at RELX rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRAGAI EvaluationNLPOpenAIAnthropic

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How do you decide that one model's output is better than another's for a given task?
What NLP problem have you worked on, and how did you measure whether it actually worked?
Walk me through how you've used OpenAI in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Prompt Engineering, RAG, AI Evaluation, NLP, and OpenAI. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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