Role Description Penguin Random House is the largest trade publishing company in the world. The Data Science team is seeking a Staff Machine Learning Scientist to lead and advance the development of personalization products, including recommender systems for our websites, email programs, and online marketing. Personalization is a core growth lever for book discovery and customer engagement, directly improving how readers find the right books across every digital touchpoint. Improving recommendation quality and relevance has a direct downstream impact on customer experience and business outcomes. We are investing in expanding our portfolio of business-critical personalization products and further improving our existing models. This role will own personalization and recommender system work end-to-end, from model development to deployment to output monitoring, in close partnership with business stakeholders, platform engineers, and the rest of the personalization group. We have a mature machine learning practice and strong infrastructure, supported by strong data warehouse and DevOps partners. We are transitioning to AI-accelerated development and use modern agentic coding tools like Claude Code to speed up how we build and maintain personalization systems, with rigorous quality gates including tests, reproducible workflows, and measurable improvements in model performance and reliability. Experience with Claude Code or agentic workflows is a plus, but we prioritize strong fundamentals and the ability and willingness to learn new workflows effectively. - Define and drive the technical roadmap for personalization and recommender systems, prioritizing roadmap items to meet business goals and defining short-term vision for the team. - Propose and deliver R&D that directly shapes roadmaps, multiple projects, and long-term deliverables. Models are used over the long term by multiple products and teams. - Design and lead the development of software used by multiple teams, ensuring long-term maintainability, scalability, and adaptability. - Ensure complex, multi-service personalization products meet SLAs and provide correct results over time. Adapt systems to changing business needs and resolve multi-product, multi-team service incidents. - Establish and enforce experimentation best practices, including A/B testing frameworks, offline evaluation methodology, and metrics design across personalization surfaces. - Lead team meetings, ensure the team's progress on the roadmap, and make technical decisions that unblock projects. - Manage stakeholders' expectations with data-driven narratives and communicate effectively with senior leadership to align on strategy and track progress. - Drive organizational efficiency and business impact by implementing new technologies and processes. Foster a collaborative and high-performance team culture. - Mentor senior and mid-level scientists, setting high code quality standards and best practices for the team. - Stay current with advances in recommender systems, LLMs for personalization, and representation learning, bringing relevant advances into production when they deliver measurable improvement. Qualifications - PhD in Computer Science, Machine Learning, Engineering, Operations Research, Statistics, or a related quantitative field, OR Master's with 8+ years of applied ML experience. - Deep expertise in recommender systems, personalization, ranking/retrieval, or computational advertising, with a track record of shipping systems that operate at scale. - Expert-level Python and deep proficiency with modern ML frameworks (PyTorch or TensorFlow) and recommendation-specific tooling (e.g., NVTabular, Merlin, Triton). - Strong experience with cloud-based ML infrastructure (AWS, Kubernetes, Databricks), containerization (Docker), and model serving at low latency. - Advanced SQL skills and experience architecting large-scale data pipelines and feature stores. - Demonstrated ability to define technical roadmaps, influence direction across teams, and make architectural decisions that hold up over time. - Excellent communication skills with the ability to present complex technical work to executive and non-technical audiences. - Be cutting edge. Use the latest AI tools to develop well-designed and robust software. Requirements - Experience building and scaling real-time recommendation services handling millions of requests. - Expertise in A/B testing methodology, causal inference, or experimentation platforms. - Familiarity with LLM-based approaches to recommendation and content understanding. - Experience with MLOps practices: model monitoring, feature stores, CI/CD for ML, and automated retraining pipelines. - Prior experience technically leading a team of ML practitioners and setting standards adopted by others. Benefits - Medical/Prescription drug insurance - Dental - Vision - Health Care/Dependent Care Flexible Spending Account - Health Savings Account - Pre-Tax and Roth 401(k) - Short and Long-Term Disability Insurance - Life/AD&D Insurance - Commuter Benefits - Student Loan Repayment Program - Educational Assistance - Generous paid time off

Role Description As the custodian agency for SDG 4 indicators, the UNESCO Institute for Statistics (UIS) is responsible for developing methodologies, maintaining global databases, supporting countries in reporting data, and producing technical guidance to improve the quality, comparability and use of education statistics. Learning assessments play a central role in SDG 4 monitoring, both for reporting on SDG indicator 4.1.1 and for producing several other indicators derived from cross-national assessment programmes. To support countries, assessment providers and policymakers, UIS develops technical guidance, decision-support tools and reporting mechanisms, including: - Criteria for reporting on SDG indicator 4.1.1 - Vetting mechanism - Learning Assessment Buyer's Guide UIS is also responsible for maintaining and disseminating a range of SDG 4 indicators derived from learning assessments and other data sources, together with their associated metadata and methodological documentation. As part of its ongoing work programme and preparations for regional consultations and the UNESCO Conference on Education Data and Statistics, UIS is seeking a consultant to support the revision and development of guidance materials and communication products, contribute to methodological work on learning indicators and indicators derived from cross-national assessment programmes, update selected SDG 4 indicator datasets and documentation, and prepare analytical products to strengthen the dissemination and use of UIS education data. Assignments Under the overall authority of the Head of the Foresight, Research and Methodology Section, the consultant will perform the following tasks: - Revision of the Buyer's Guide to International Student Assessments (2025 edition): Review documents and inputs, consult with partner organizations, incorporate feedback, and draft a revised Buyer's Guide. - Learning Assessments Online Decision Tree: Develop a concept and implementation proposal for an interactive online decision tree. - Vetting Mechanism communication document: Develop a concise communication document to support the positioning and promotion of the UIS Vetting Mechanism. - Discussion Paper on learning indicators: Prepare a discussion paper reviewing key methodological, conceptual and implementation challenges related to learning indicators. - Technical Note on indicators derived from cross-national assessments: Prepare a technical note reviewing methodologies used to produce SDG indicators derived from cross-national assessment programmes. - Update of SDG indicators and metadata: Update datasets for selected indicators, validate and revise datasets, and document all revisions made. Contract Duration The consultancy will be carried out over a period of 5 months, with an expected start date shortly after the completion of the selection process. The assignment will be home-based (remote), with deliverables submitted according to the schedule below. Deliverables - Revised Learning Assessment Buyer's Guide: Draft framework and recommendations by July 2026; final revised Buyer's Guide by August 2026. - Learning Assessments Online Decision Tree: Concept and implementation proposal by August 2026. - UIS Vetting Mechanism communication document: Communication document by August 2026. - Discussion Paper on learning indicators: Discussion paper by August 2026. - Technical Note on indicators derived from cross-national assessment programmes: Technical note by September 2026. - Statistical fact sheet on bullying prevalence: Fact sheet by September 2026. - Updated SDG indicator datasets and documentation: Updated datasets and documentation by October 2026. Qualifications - Advanced university degree (Master's or equivalent, or higher) in statistics, demography, economics, mathematics or related domains. Requirements - A minimum of 8 to 10 years of relevant work experience in applied social statistics, ideally related to the assessment of learning outcomes. - Preferably 3 to 5 years acquired at the international level within a UN or similar agency, or a government ministry. - Demonstrated expertise in education statistics, learning assessments and SDG 4 monitoring. - Strong analytical skills, including the ability to review indicator methodologies, validate datasets and assess comparability across data sources. - Excellent drafting and communication skills. - Solid knowledge of cross-national learning assessment programmes and of the SDG 4 monitoring framework. - Ability to work independently in a remote setting, manage multiple concurrent deliverables and meet tight deadlines. - Proven research experience in education is desirable. Languages - Excellent proficiency in English (written and spoken) is required. - Knowledge of other official UNESCO languages (French, Spanish, Arabic, Chinese, Russian) is an asset. Application Process Interested candidates should complete the on-line application, download and complete the Employment History form. At the end of the Word file, insert extra pages with the following required information: - Part 1: Technical Proposal - An up-to-date curriculum vitae; - A statement indicating how their qualifications and experience make them suitable for the assignment; - An indication of the approach (methodology, detailed workplan) he/she would adopt to carry out the assignment; - Part 2: Financial Proposal - A competitive fee for the assignment, quoted in EUR/USD/CAD/GBP and expressed on a monthly basis. Selection and Recruitment Process All candidates must complete an on-line application and provide complete and accurate information. The process may include pre-recorded video interviews and/or written assessments, interviews with a Panel, as well as reference checks. Additional Information UNESCO recalls that paramount consideration in the appointment of personnel shall be the necessity of securing the highest standards of efficiency, technical competence and integrity. UNESCO applies a zero-tolerance policy against all forms of harassment. Individuals from minority groups and indigenous groups and persons with disabilities are equally encouraged to apply. All applications will be treated with the highest level of confidentiality. UNESCO does not charge a fee at any stage of the hiring process.

• Define system architecture for AI/LLM-powered products end to end over claims, medical records, and clinical documentation. • Build and own evaluation frameworks (LLM-as-a-Judge, offline metrics, online experiments) aligned to accuracy, auditability, and clinical and regulatory risk. • Drive the data flywheel: convert expert clinician and auditor review decisions into high-quality labeled data. • Lead ranking and prioritization systems that surface the highest-value claims, audits, and care gaps for human review. • Establish reusable platform patterns — shared context stores, evaluation harnesses, feature pipelines.

Role Description We're looking for a hands-on and forward-thinking Machine Learning Engineer. In this role, you will be supported by a dedicated team, equipped to aid your success and bolster our rapid growth. At Critical, we deliver software solutions and consulting in complex, business-critical environments aimed at assisting our clients in achieving their business objectives through cutting-edge software solutions. The ideal candidate will design, build, and deploy machine learning models and pipelines that solve real business problems across various departments. You will work hands-on across the full ML lifecycle — from data preparation and model development to deployment and monitoring — collaborating closely with stakeholders to turn their needs into robust, production-ready solutions. Qualifications - Bachelor's or Master's in Computer Science, AI, Machine Learning or a related field. - 5+ years of experience in software development, with hands-on experience building and deploying machine learning models. - Solid coding skills (Python and relevant ML frameworks) and experience with the ML lifecycle from data to production. - Clear oral and written communication skills for working with teammates and stakeholders. - Practical, detail-oriented approach to debugging and improving models and pipelines. - Ability to manage your own tasks and prioritize effectively, even with some ambiguity. - A natural interest in ML techniques — old and new — and how they apply to real business problems. Requirements - Design, train, and evaluate machine learning models to address specific business problems. - Build and maintain data pipelines and infrastructure to support model development and deployment. - Deploy ML models into production and monitor their performance, reliability, and drift over time. - Identify and resolve technical issues, bugs, and blockers as they arise during development and deployment. - Work closely with team members across various departments to understand their data, processes, and needs, and adapt solutions accordingly. - Iterate on deployed models to keep them accurate, efficient, and useful as needs evolve. Benefits - Private health insurance - Employee Assistance Programme (mental health, legal, financial support) - Home office support - Extra holidays: 2 additional days after year one, more as time goes on - Extra parental leave: 2 additional months fully paid - Gradual return-to-work support: We'll help you ease back from long breaks - Sabbatical programme for long-term employees - Training, mentorship, and growth opportunities: we'll invest in where you want to go next