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Manager Data Science**Home based San Francisco, CA

Role overview

Qualifications

  • Strong understanding of transformer architectures and attention mechanisms
  • Strong Python and PyTorch skills with experience using Hugging Face Transformers
  • Demonstrated ability to implement supervised fine-tuning and configure training objectives
  • Practical experience with parameter-efficient fine-tuning including LoRA or QLoRA

Responsibilities

  • Lead the design and execution of LLM training and fine-tuning projects
  • Oversee the preparation of high-quality training datasets
  • Develop and optimize supervised fine-tuning and parameter-efficient fine-tuning workflows
  • Partner with product, engineering, and domain experts to define requirements

Key facts

Hard skills

Other skills

  • Team Management
  • Communication
  • Problem Solving
  • Mentorship

About the company

LexisNexis Risk Solutions logo

LexisNexis Risk Solutions

Data Analytics & Business Intelligence

LexisNexis® Risk Solutions includes seven brands that span multiple industries. Our portfolio includes the LexisNexis Risk Solutions go-to-market brand and our six Data Services brands that provide customers with innovative technologies, information-based analytics, decisioning tools and data management services that help them solve problems, make better decisions, stay compliant, reduce risk and improve operations.Our well-known brands are LexisNexis® Risk Solutions, ICIS®, Cirium®, Proagrica™, XpertHR®, EG™ and Nextens® and serve a variety of sectors including aviation, agriculture, chemical and energy, financial services, collections and payments, commercial property, corporations and non-profits, government and law enforcement agencies, healthcare, human resources, insurance and tax.We harness the power of data, sophisticated analytics platforms and technology solutions to provide insights that help businesses and governmental entities reduce risk and improve decisions to benefit people around the globe. Headquartered in metro Atlanta, Georgia, we have offices throughout the world and are part of RELX (LSE: REL/NYSE: RELX), a global provider of information-based analytics and decision tools for professional and business customers. For more information, please visit risk.lexisnexis.com and www.relx.com.Follow our brands: LexisNexis Risk Solutions: https://www.linkedin.com/company/lexisnexis-risk-solutions/ ICIS: https://www.linkedin.com/company/icisCirium: https://www.linkedin.com/company/cirium/ Proagrica: https://www.linkedin.com/company/proagrica/ XpertHR: https://www.linkedin.com/company/xperthr EG: http://www.linkedin.com/company/estatesgazette.com Nextens: https://www.linkedin.com/company/nextens/

Company details

Company typeXLarge
IndustryData Analytics & Business Intelligence
Company size10001

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Job description

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.




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.







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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