Senior Data Scientist (Maternity cover)

Global-e, a leader in cross-border e-commerce, as a Senior Data Scientist. In this maternity cover role, you will be a key member of our classification team, working on our machine learning-based classification system. Your responsibilities will include researching, designing, maintaining, and evolving our machine learning models, developing and deploying Large Language Models, and partnering closely with product, engineering, and other business leaders.

About Global-e

Partner closely with product, engineering and other business leaders by providing consulting and analytic services that help influence product decisions with data.

Responsibilities

• You will also document insights and analysis for stakeholders, evaluate model performance, and help cultivate a culture of experimentation.
• Research, design, maintain, and evolve the company's machine learning models to support the world’s fastest and most accurate classification and restriction systems for cross-border trade.
• Develop, fine-tune, and deploy Large Language Models (LLMs) to enhance classification accuracy and automation capabilities.
• Experience in optimizing and scaling deep learning models for real-world deployment in production environments

Requirements

Strong data visualisation skills

• Solid programming skills to code, build, and test models
• Familiarity with transformer-based architectures (e.g., BERT, GPT) and their application to classification and predictive modeling
• At least 5 years of experience in data science, solving real-world problems, and using modern technologies to develop ML models
• Experience with training, fine-tuning, and deploying LLMs, as well as applying state-of-the-art architectures to domain-specific challenges
• Experience with the full lifecycle of a data product - ideation to productionizing and iterating
• Excellent communication skills, both written and verbal; the ability to convey your message to team members and other stakeholders

Preferred

• Experience with NLP algorithms and approaches and their applications to taxonomies, recommendations, and search domains

Knowledge of and appetite for mathematical principles that are the foundation of ML methods