Principal Data Scientist I
AI in this role
Principal Data Scientist I
Are you looking to develop your Data Scientist career?
Would you like to be part of a team that innovates using technology?
Responsibilities
- Acting as a leader for analytic project and result sharing
- Leading code reviews, acting as a final approver of models before reaching customers
- Evaluating and creating new frameworks and defining methodologies and governance
- Managing and leading small-to-medium sized teams, directly or indirectly
- Setting a high-bar for model development
Requirements
- Large language models (prompting, fine-tuning, adaptation), LLM
- Advanced retrieval-augmented generation (hybrid search, ranking optimization), RAG
- Embedding strategies and semantic search systems
- Knowledge graph integration and graph-enhanced intelligence
- Agentic AI architecture, multi-step reasoning, autonomous agent orchestration, and long-running agent design with feedback loops, GenAI
- Evaluation science (offline metrics, human-in-the-loop systems)
- Cost-performance optimization
- AI observability and monitoring in production
- Data governance and compliance-aware system design
- Have excellent communication skills and the ability to convey information clearly to senior stakeholders
- Be a natural problem-solver and able to take a lead in collaborating to resolve issues
- Have experience in mentoring or training others, and acting as a subject matter expert to guide colleagues
- Have some management experience, including leading a team indirectly or directly
- Have significant experience in a Data Scientist role
- Have experience in leading complex projects and customer relationships
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How we rate this
Principal Data Scientist I at RELX rates 93 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.
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
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: RAG and Fine Tuning. 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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