Lead Data & AI Scientist

Job Description Summary


Responsibilities

• Project Delivery: Oversee the entire data science / AI delivery lifecycle, from data exploration to model development/tuning to deployment, monitoring, and performance optimisation using ML and AI Ops best practices.
• Customer Communications: Lead the team in creating predictive models that identify customer needs and help drive more relevant, timely and personalised communications.
• AI Solution Design: Lead the development of AI use-case solution designs, balancing innovation with practicality. Apply appropriate data science techniques to develop and/or tune models and evaluate performance.
• Team Mentorship: Coach and mentor data scientists, helping to grow team capabilities and fostering a culture of continuous learning and curiosity.
• Collaboration: Lead and work within cross-functional teams comprising of business leaders, product managers, data scientists, data engineers and ML/AI engineers
• Continuous Learning: Maintain an active view of emerging tools and techniques. Develop proof-of-concepts to demonstrate new capabilities. Guide the team in solving complex AI use cases and improving existing approaches.
• Responsible AI Governance: Partner with the Responsible AI team to embed robust ethical principles, bias mitigation techniques, and regulatory compliance into all AI development workflows.

What You'll Need

• Degree in Computer Science, AI, Data Science, or a related field or equivalent experience.
• 5 or more years of experience in data science or AI roles.
• Experience leading a multidisciplinary team of Data Scientists and Machine Learning Engineers.
• Strong understanding of cloud platforms (GCP preferred).
• Excellent communication and stakeholder management skills.

Recruitment Priority Skills

• Machine Learning: Experience to situationally assess and advise on the most appropriate Machine Learning solution to tackle existing and new scenarios. Troubleshoots and fixes model performance issues with the fitted models. Is able to use advanced methods such as forecasting, ensemble methods and model stacking.
• Python: Has an holistic understanding of Python and can make intuitive decisions in familiar contexts. Capable of implementing multi-threading and asynchronous programming using tools like threading and asyncio. Optimize performance using profiling tools and apply advanced debugging techniques.
• AI Literacy: Designing workflows that integrate AI, anticipating AI behaviour, and adapts usage creatively. Coaches others and contributes to AI strategy discussions.
• Googe Cloud Platform: Capable of end-to-end delivery of products on GCP following standard architectures and templates. Takes key decisions on implementation.
• Foundation Model Application: Possesses experience with Foundation Models. Understands the theoretical underpinnings of Foundation Models and engages in creative problem-solving using them. Can prioritise decisions effectively, balancing innovation with practicality to solve complex challenges.

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