Utilise your deep understanding of one or more sub-areas of applied ML, developing deep expertise in advanced research techniques while building broad understanding of related services, platforms, and domains. Identify emerging product needs by continuously tracking industry trends and applied technologies. Translate business requirements into research-driven solutions, shaping the strategic direction for data usage and applying deep subject-matter knowledge to deliver measurable product and business impact. Be responsible for the technical excellence across the team by onboarding and mentoring junior scientists, supporting their growth into multidisciplinary contributors, and helping develop future talent through collaboration with academic partners. Design, run, and document experiments end-to-end, clearly communicating insights, trade-offs, and results to promote innovation, influence design choices, and accelerate research-to-product delivery. Commit to responsible AI practices, applying strong understanding of fairness, bias, and privacy-preserving techniques to influence data collection, model development, and research processes. Collaborate closely with engineering, product, and cross-org partners to operationalize research work, align on requirements, and ensure models and techniques are production-ready at integral scale. Continuously refine scientific and engineering rigor, contributing to frameworks, tooling, and best practices that improve reproducibility, efficiency, and quality across research workflows. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND related experience (e.g., statistics, predictive analytics, research) Proven experience owning the full lifecycle of production ML systems, from research through deployment. Strong Python and/or C++ skills and experience with PyTorch or TensorFlow. Production inference technologies (e.g., ONNX) are a plus. Experience conducting applied research that operates under real-world constraints, including personalization, global linguistic diversity, privacy-preserving learning, and mobile-oriented research contexts (e.g., on-device modeling, resource-constrained inference). Strong English written and verbal communication skills. Master's Degree in Statistics, Econometrics, Computer Science, Electrical Experience creating publications (e.g., patents, libraries, peer-reviewed academic papers). Experience presenting at conferences or other events in the outside research/industry community as an invited speaker. Experience conducting research as part of a research program (in academic or industry settings).