Senior Data Scientist II

Remitly•Raleigh, NC

About The Position

About the Team: LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html). About the Role: Are you looking to develop your Data Scientist career? Would you enjoy training more junior team members? As a Senior Data Scientist II, you will need to independently design and deliver effective AI solutions for data scientist projects. Your work will involve collaborative problem solving, technical leadership, and mentoring of junior team members.

Requirements

Technical Expertise: Hands-on experience with several areas such as information extraction, long-context modeling, entity linking, and retrieval systems.
Programming Skills: Proficiency in Python and experience with ML frameworks and libraries, including PyTorch/Transformers, spaCy, and vector search libraries.
Analytical Skills: Advanced skills in evaluation design, error analysis, and a capability to defend trade-offs using clear metrics.
MLOps: Experienced with deploying production-grade machine learning models and managing end-to-end pipelines.

Nice To Haves

Domain Experience: Prior work experience in regulated domains or industries with complex data challenges.
Advanced Techniques: Familiarity with knowledge-graph approaches, document clustering, or programmatic labeling.
Agentic Solutions: Knowledge of agentic frameworks and capabilities in building agentic solutions is a valuable plus.
Communication: Excellent written and verbal communication skills for effective collaboration with technical and non-technical stakeholders.

Responsibilities

Modeling & Methods Algorithm Design: Develop and implement advanced models for tasks such as information extraction, classification, and entity linking.
Innovative Techniques: Work with long-context modeling, coreference resolution, retrieval/re-ranking systems, and LLM structured output.
Experimentation: Lead in designing baselines, comparators, ablation studies, and rigorous evaluation strategies.
Data & Evaluation Data Quality: Oversee data collection and annotation processes to build high-quality datasets.
Metrics: Define and measure key performance metrics (e.g., Precision, Recall, F1, ranking metrics) to evaluate model performance.
Analysis: Conduct comprehensive error analysis and perform iterative model enhancement.
Engineering & Delivery Product ionization: Implement and deploy models in both batch and real-time environments.
System Integration: Collaborate with engineering teams to build end-to-end data pipelines and scalable production systems.
Documentation: Write detailed design documents and ensure reproducibility of the entire modeling pipeline.
Standards & Mentorship Best Practices: Establish and propagate coding, evaluation, and model deployment standards across teams.
Collaboration: Engage with cross-functional teams including product, engineering, and domain experts.
Leadership: Mentor junior data scientists, review work, and foster a culture of continuous improvement within the team.