2 days ago
London, United KingdomSenior
Responsibilities
- Build and deploy production-grade machine learning software, tools, and infrastructure.
- Create reusable and scalable solutions that accelerate delivery of AI and machine learning systems.
- Collaborate with engineers, data scientists, product teams, commercial leads, clients, and partners to solve business challenges.
- Lead technical scoping and architectural decisions for feasible, scalable, commercially impactful solutions.
- Define and implement standards for deploying machine learning systems in production.
- Advise customers and partners by translating complex ML concepts into actionable business outcomes.
- Apply machine learning to demand forecasting, customer analytics, personalisation, pricing, marketing optimisation, inventory management, and operational efficiency.
- Support clients in adopting AI capabilities that improve customer experience and sustainable growth.
Requirements
- Experience across the full machine learning lifecycle and operationalising models built with Scikit-learn, TensorFlow, or PyTorch.
- Strong Python skills and solid software engineering best practices.
- Hands-on experience with cloud platforms and infrastructure such as AWS, Azure, or GCP, including architecture and security.
- Experience with Docker and Kubernetes for building and managing applications at scale.
- Understanding of probability, statistics, experimentation, and common machine learning techniques.
- Experience with retail, consumer, ecommerce, marketing, supply chain, or customer data is beneficial but not required.
- Strong communication skills and the ability to guide technical teams and advise non-technical stakeholders.
- Ability to work autonomously, own scope, and solve challenging problems in a fast-paced environment.
Benefits
- Part-time hours may be discussed.
- The interview process includes a Talent Team screen, pair programming interview, system design interview, and commercial interview.
- Candidates may opt out of the AI note-taker used during interviews.
Tech Stack
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