Senior Data Science Lead

Role Overview

• Own the technical and research direction for the pod, spanning data science, machine learning, and advanced AI research, including feature engineering, model and algorithm selection, experimental design, evaluation, and transition approaches.
• Define and guide research programs and experiments, balancing hypothesis‑driven exploration with enterprise relevance and downstream applicability.
• Ensure solutions and research outcomes are scalable, maintainable, and aligned with enterprise architecture, data, security, and engineering standards.
• Review, challenge, and approve technical designs, experiments, research findings, and AI artefacts produced by the pod.
• Drive efficiency and quality through the adoption of Generative AI tooling, automation, reusable research patterns, and shared platforms.
• Establish expectations for scientific rigor, documentation, reproducibility, and evaluation consistency.
• Lead pod‑level planning and execution across research and delivery activities, aligning exploration, experimentation, and milestones to the broader product, technology, and IT roadmaps.
• Balance experimentation with execution, ensuring effective transition from research and proof‑of‑concept work to production‑ready or downstream engineering solutions.
• Partner with the IT organisation to ensure research outcomes can be operationalised, supported, and scaled within enterprise platforms and environments.
• Actively identify, manage, and communicate technical risks, research uncertainties, dependencies, and trade‑offs.
• Provide day‑to‑day and senior‑level technical leadership, coaching, and mentoring for pod members.
• Support development of skills across data science, machine learning, Generative AI, advanced AI research methods, and software engineering practices.
• Foster a culture of collaboration, accountability, learning, peer review, and continuous improvement within the pod.

Requirements

• Bachelor’s degree in data science, computer science, engineering, mathematics, statistics, or a related field (or equivalent practical experience)
• Advanced degree (Master’s or PhD preferred) in artificial intelligence, machine learning, engineering, mathematics, physics, or a closely related field, or equivalent depth of practical research experience is required.
• Strong understanding of AI, machine learning, and Generative AI concepts, risks, and opportunities.
• Demonstrated depth in one or more advanced or emerging areas such as multimodal models, embodied or physical AI, reinforcement learning, simulation‑based learning, or quantum‑inspired algorithms.
• Ability to design, interpret, and guide experiments and model evaluations to inform technical and research decisions.
• Ability to translate business, technology, and IT strategy into outcome‑driven research themes, product goals, and measurable value hypotheses.
• Experience working in Agile or iterative delivery environments, including sprint planning, backlog refinement, and incremental delivery.
• Excellent verbal and written communication skills, with the ability to explain complex technical and research concepts to non‑technical stakeholders.
• Strong problem‑solving skills with the ability to think critically and creatively.
• Knowledge of relevant programming languages, tools, and prompt engineering.
• Strong understanding of AI risks, limitations, and ethical considerations.

Benefits

• Professional growth opportunities
• Leadership programs
• Diversity and inclusion culture