• Build, deploy, and maintain production systems that power Northbeam's measurement products. • Translate statistical and causal inference methodologies into scalable, reliable customer-facing capabilities. • Improve the accuracy, coverage, reliability, and operational robustness of our measurement systems. • Debug data, modeling, and production issues across the full stack, from source data to customer-facing outputs. • Evaluate methodological improvements pragmatically, balancing statistical rigor, implementation complexity, maintainability, and business value. • Partner closely with teammates across Data Science, Engineering, Product, and Customer Success to improve measurement quality and product capabilities. • Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to improve Northbeam's products.

• Build, deploy, and maintain production systems that power Northbeam's measurement products. • Translate statistical and causal inference methodologies into scalable, reliable customer-facing capabilities. • Improve the accuracy, coverage, reliability, and operational robustness of our measurement systems. • Debug data, modeling, and production issues across the full stack, from source data to customer-facing outputs. • Evaluate methodological improvements pragmatically, balancing statistical rigor, implementation complexity, maintainability, and business value. • Partner closely with teammates across Data Science, Engineering, Product, and Customer Success to improve measurement quality and product capabilities. • Explain statistical concepts to customers, troubleshoot measurement issues, and gather feedback to improve Northbeam's products.

Role Description - Design and manage search recommendation services and models to support the content team, empowering content ecosystem development. - Utilize machine learning-based personalization methods, design patterns, and tools to enhance content matching and distribution efficiency. - Deep understanding of recommendation systems within the content domain, leveraging business knowledge to drive AI product upgrades and improve content experience and business outcomes. - Lead the development of core content recommendation modules using data-driven strategies to maximize content value and user impact. - Collaborate with content, business, and product management teams to identify needs and opportunities within content scenarios, and jointly define success metrics. Qualifications - MSc or PhD in Machine Learning, Computer Vision, Computer Science, or Applied Mathematics, with at least 5 years of relevant industry experience (experience in the content domain is preferred). - Strong hands-on experience with popular machine learning frameworks such as PyTorch or TensorFlow. - Experience in large-scale application development, preferably in content-related user-facing applications. - Excellent cross-team collaboration skills with the ability to work effectively with global content teams. - Publication record at top-tier conferences or journals is a plus. Benefits - Shape the future with the world’s leading blockchain ecosystem. - Collaborate with world-class talent in a user-centric global organization with a flat structure. - Tackle unique, fast-paced projects with autonomy in an innovative environment. - Thrive in a results-driven workplace with opportunities for career growth and continuous learning. - Competitive salary and company benefits. - Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team).

• Lead the end-to-end design, development, and implementation of new and innovative rating plans for personal auto and home insurance products. • Build and refine pricing models using GLMs, GBMs, and other ML techniques to sharpen risk segmentation and deliver competitive price accuracy. • Drive feature engineering efforts and identify, evaluate, and integrate external data sources to enhance model performance and predictive power. • Design and build efficient, scalable, and repeatable processes for model development, data pipelines, and rating plan deployment. • Own the technical documentation, validation, and peer review of rating plan changes to ensure accuracy and compliance. • Partner closely with Product, Data Science, Engineering, Underwriting, and other business and analytical teams to develop and implement changes. • Mentor and provide technical guidance to junior actuaries and data scientists, fostering a collaborative and high performance team culture.