Manager Data Science**Home based San Francisco, CA

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.

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.

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.

• →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.

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 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.

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