Manager Data Science**Home based San Francisco, CA
At a glance
Manager-level Data Science role at RELX (LexisNexisLegal). HB San Francisco, CA · full-time · $115,400–$192,300 base.
dataroles summary, based on the employer's posting.
What you'll do
- Lead LLM training projects from model choice and experiments through fine-tuning and evaluation
- Prepare training data by collecting, cleaning, deduplicating, annotating and checking its quality
- Build evaluation methods for accuracy, instruction following, safety and domain-specific performance
- Improve training stability, model quality and compute efficiency by diagnosing technical issues
- Guide data scientists, coordinate with product and engineering, and manage priorities, timelines and compute
What you bring
- You understand transformer models, attention, tokenization and how pretraining differs from fine-tuning
- You bring strong Python and PyTorch skills, plus hands-on LLM fine-tuning experience
- You have used PEFT methods such as LoRA or QLoRA and understand their compute and memory tradeoffs
- You can prepare instruction datasets, train across multiple GPUs, evaluate model quality and debug training issues
- You have experience tracking experiments, managing versions and checkpoints, and documenting repeatable training workflows
Who this fits
This manager role suits someone who can lead and develop data scientists while remaining hands-on with LLM training. The role is home based in San Francisco, California, with flexible working hours.
From the employer
About the Business
LexisNexis Legal & Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision-making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services.
About the Role
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.
Requirements
- LLM fundamentals: Strong understanding of transformer architectures, attention mechanisms, tokenization, language modeling objectives, and the differences between pretraining, continued pretraining, and fine-tuning.
- Programming and frameworks: Strong Python and PyTorch skills, with practical experience using Hugging Face Transformers, Datasets, or equivalent tools.
- Hands-on LLM training: Demonstrated ability to implement supervised fine-tuning (SFT), configure training objectives and loss masking, tune hyperparameters, and select model checkpoints.
- Efficient fine-tuning: Practical experience with parameter-efficient fine-tuning (PEFT), including LoRA or QLoRA, and an understanding of their quality, memory, and compute tradeoffs.
- Training data engineering: Ability to build instruction-response datasets, apply chat templates, manage sequence lengths and packing, and prevent data leakage and evaluation contamination.
- GPU and distributed training: Experience training models across multiple GPUs using frameworks such as PyTorch FSDP or DeepSpeed, including mixed precision, gradient accumulation, and gradient checkpointing.
- Evaluation and debugging: Ability to design reliable benchmarks and human evaluations, analyze model errors, and troubleshoot unstable loss, overfitting, and GPU memory issues.
- Reproducibility: Experience with experiment tracking, dataset and model versioning, checkpoint management, and documented training pipelines.
Work in a Way That Works for You
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.
About the Business
LexisNexis Legal & Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision-making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services.
We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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