About
Data Science Strategy & Delivery
Lead mission-critical data initiatives from discovery to deployment and continuous improvement, with clear success metrics and guardrails.
Modelling, Productionisation & Standards
Apply and contribute to best practices for experimentation, modelling and measurement (including uplift/causal methods), ensuring reproducibility, versioning and lineage.
Review and raise the standards on critical models (e.g. feature engineering, validation, bias/leakage prevention); translate research into pragmatic, production-ready methods.
Partner with machine learning and AI engineers to productionise robust batch/real-time services following best practices; embedding monitoring, drift detection, explainability and fairness standards.
Technical Leadership, Influence & Collaboration
Act as a technical coach and mentor across squads - mentoring time is supported; provide design/analysis reviews and uplevel senior ICs and early-career talent.
Influence roadmaps by collaborating with Product, Engineering and Data leaders on priorities and trade-offs; represent Data Science in architecture and design forums.
Contribute to the AI/ML platform direction by shaping requirements and partnering with platform owners; drive adoption of reproducible, reliable workflows.
Culture & Innovation
Foster a culture of learning, transparency and cross-functional collaboration; share decisions, assumptions and outcomes openly.
Evaluate emerging research and market trends (e.g., LLMs, recommender / optimisation methods, knowledge graph, transformers) and turn promising ideas into prototypes and patterns.
Embed responsible AI in day-to-day delivery-explainability, fairness and compliance-and promote continuous improvement through demos and write-ups.
Qualifications
Proven experience delivering high-impact ML/AI solutions in complex, data-rich environments, including production deployment and post-launch iteration.
Advanced hands‑on proficiency in Python and core DS/ML libraries; strong SQL and familiarity with distributed data tooling.
Strong understanding of experimentation and statistical inference; experience designing trustworthy A/B tests and measurement frameworks.
Strong grasp of ML system design and the model lifecycle (from discovery and experimentation through to deployment, monitoring and governance).
Ability to lead cross‑functional technical work with multiple stakeholders and to influence without authority.
Excellent communication and storytelling skills, able to convey complex ideas simply and align teams on decisions.
Experience mentoring other data scientists and shaping best practices across a team.
Background in a quantitative field (e.g., statistics, computer science, mathematics, engineering) or equivalent applied experience.
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Languages
- English
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