[Hiring] Manager Data Science @RELX

Role Description

A Manager Data Science is an emerging subject matter expert in their domain. They lead a team of junior members to support their development and work product. They are mindful of best practices and train their team in the execution of those best practices. They manage a team to define new best practices and innovative approaches to new business problems or use cases.

Responsibilities

• Lead the design and execution of LLM training and fine-tuning projects, including model selection, training strategy, experimentation, and evaluation.
• Oversee the preparation of high-quality training datasets, including data collection, cleaning, deduplication, annotation, and quality validation.
• Develop and optimize supervised fine-tuning and parameter-efficient fine-tuning workflows; apply preference optimization methods where appropriate.
• Establish evaluation frameworks to assess factual accuracy, instruction following, domain relevance, safety, and performance on business-specific tasks.
• Diagnose training issues and improve model quality, training stability, GPU utilization, and computational efficiency.
• Manage and mentor data scientists, review technical work, and establish reproducible development practices.
• Partner with product, engineering, and domain experts to define requirements and support model deployment and monitoring.
• Manage project priorities, timelines, and compute resources, and communicate results and tradeoffs to stakeholders.

Qualifications

• Strong understanding of transformer architectures, attention mechanisms, tokenization, language modeling objectives, and the differences between pretraining, continued pretraining, and fine-tuning.
• Strong Python and PyTorch skills, with practical experience using Hugging Face Transformers, Datasets, or equivalent tools.
• Demonstrated ability to implement supervised fine-tuning (SFT), configure training objectives and loss masking, tune hyperparameters, and select model checkpoints.
• Practical experience with parameter-efficient fine-tuning (PEFT), including LoRA or QLoRA, and an understanding of their quality, memory, and compute tradeoffs.
• Ability to build instruction-response datasets, apply chat templates, manage sequence lengths and packing, and prevent data leakage and evaluation contamination.
• Experience training models across multiple GPUs using frameworks such as PyTorch FSDP or DeepSpeed, including mixed precision, gradient accumulation, and gradient checkpointing.
• Ability to design reliable benchmarks and human evaluations, analyze model errors, and troubleshoot unstable loss, overfitting, and GPU memory issues.
• Experience with experiment tracking, dataset and model versioning, checkpoint management, and documented training pipelines.

Requirements

• We promote a healthy work/life balance across the organisation.
• We offer an appealing working prospect for our people.
• With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.

Working Pattern

• Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive.

Benefits

• U.S. National Base Pay Range: $115,400 - $192,300. Geographic differentials may apply in some locations to better reflect local market rates.
• This job is eligible for an annual incentive bonus.
• Country specific benefits available.