Advanced Specialist, AI Scientist

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Pearson VUE

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1994

📚 Education

🛍️ eCommerce

☁️ SaaS

Education • eCommerce • SaaS

Pearson VUE is a global leader in computer-based testing, providing a wide range of credentialing and certification exams for various industries. They support test-takers and test owners by offering resources, scheduling options, and accommodations to ensure equitable access to testing. Their mission is to empower candidates and enrich communities through the delivery of high-stakes exams that validate professional skills and knowledge, contributing to career advancement and industry standards.

📋 Description

• Deliver robust, scalable ML solutions for AI capabilities in educational products, taking significant implementation decisions independently. • Work closely with Business Unit partners to understand their AI-related problems, help frame them clearly, and develop solutions that fit their actual needs. • Identify opportunities to reuse existing AI components and capabilities across teams. This includes use case discovery, technical solutioning, and making a credible case to partner teams for why reuse is the right path rather than building from scratch. • Review and validate the outputs of more junior scientists, providing expert-level technical feedback and catching issues before they reach production. • Shape the direction of projects within the team, bringing experience and a point of view to decisions about approach, tooling, and scope. • Propose new techniques and methods where current approaches have clear limitations, and take responsibility for evaluating and introducing them. • Mentor peers and support the growth of colleagues at earlier career stages, through code reviews, pairing, and direct feedback. • Maintain thorough documentation and uphold the technical health of deliverables over time, not just at point of delivery.

🎯 Requirements

• Strong applied experience in machine learning and deep learning, with a track record of delivering solutions that have gone into production and affected real outcomes. • Effective proficiency in Python and core ML frameworks (e.g. PyTorch, TensorFlow, Hugging Face, scikit-learn), including model evaluation, experimentation, and iteration at scale. • Experience working directly with non-technical or semi-technical stakeholders to understand problems, frame requirements, and align on the right technical approach. • Ability to assess an existing capability or component critically and make a reasoned case for whether it fits a new context. • Comfortable shaping project direction and making significant technical decisions with appropriate autonomy. • Experience reviewing others' work at a high level of rigour, and providing feedback that improves both the output and the person who produced it.