Senior Data Scientist

LexisNexis

. Lead the design and execution of LLM training and fine-tuning projects, including model selection, training strategy, experimentation, and evaluation .

Posted 11/10/2026full-timeRemote • California • United StatesSenior💰 $115,400 - $192,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading LLM training and fine-tuning projects, with a strong focus on model selection, training strategies, and evaluation frameworks. Proficient in managing data scientists and collaborating with cross-functional teams to ensure successful model deployment and monitoring.

Highest-signal resume keywords

LLM Training And Fine-TuningPython ProgrammingPyTorch FrameworkHugging Face TransformersSupervised Fine-Tuning

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Transformer ArchitecturesAttention MechanismsTokenizationLanguage Modeling ObjectivesParameter-Efficient Fine-TuningMixed Precision TrainingGradient AccumulationExperiment TrackingDataset VersioningModel Checkpoint Management

Soft Skills

MentoringCommunicationProject Management

Tools & Technologies

Hugging Face DatasetsPyTorch FSDPDeepSpeed

Industry Keywords

Model EvaluationTraining StabilityGPU UtilizationData Leakage PreventionBenchmark Design

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

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 through 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

What you’ll need

Strong understanding of transformer architectures, attention mechanisms, tokenization, language modeling objectives, and differences between 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, 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
Experience managing and mentoring data scientists
Experience partnering with product, engineering, and domain experts
Ability to manage project priorities, timelines, and compute resources
No explicit educational credential or minimum years of experience stated

Benefits

Comp & perks

Annual incentive bonus
Healthy work/life balance initiatives
Wellbeing initiatives
Shared parental leave
Study assistance
Sabbaticals
Flexible working hours
Country specific benefits
Disability accommodations during hiring process