Manager, Data Science

🕒 September 22

🏄 California – Remote

🏄 California – Remote

💵 $115.4k - $192.3k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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RELX

WebsiteLinkedInAll Job Openings

10,000+ employees

💼 Consulting

🏥 Healthcare

🛡️ Insurance

Consulting • Healthcare • Insurance

RELX is a global provider of information-based analytics and decision tools for professional and business customers. The company focuses on enabling its clients to make better decisions, improve results, and enhance productivity by leveraging advanced technology and data. RELX serves various sectors, including Risk, Scientific, Technical & Medical, Legal, and Exhibitions, by offering specialized information and analytical tools that facilitate critical decision-making. The company is committed to corporate responsibility and delivering societal benefit through its products by contributing to scientific advancement, legal justice, and effective market transactions.

📋 Description

• Lead the design and execution of LLM training and fine-tuning projects, including model selection, training strategy, experimentation, and evaluation • Oversee preparation of 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 for factual accuracy, instruction following, domain relevance, safety, and business-specific performance • 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 • Communicate results and tradeoffs to stakeholders

🎯 Requirements

• Strong understanding of transformer architectures, attention mechanisms, tokenization, language modeling objectives, and pretraining, continued pretraining, and fine-tuning • Strong Python and PyTorch skills • Practical experience with 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 • 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 PyTorch FSDP or DeepSpeed • Experience with 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

🏖️ Benefits

• Annual incentive bonus • Flexible working hours • Wellbeing initiatives • Shared parental leave • Study assistance • Sabbaticals • Country specific benefits • Work/life balance support • Accommodation or adjustment support for applicants with disabilities

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