Utility Warehouse
Utilities
SeniorMachineLearningEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Senior Machine Learning Engineer at Utility Warehouse. Skills: Machine Learning, MLOps, GenAI, RAG. Deploy robust ML models. Develop Customer Segmentation”
What You'll Achieve.
Deliver scalable, reliable software; Optimize acquisition spend; Optimize retention spend; Ensure model performance; Ensure model drift monitoring; Validate model effectiveness; Measure commercial uplift
Industry & Context.
Strategic Problem Solving; Evaluate proposed work; Provide critical feedback
On-call rotations
What They're Looking For.
Must Have
Production ML Experience, Python proficiency, Software engineering best practices, Unit testing, Modular code, Git proficiency, Docker experience, Kubernetes experience, MLOps Tooling experience, Model registries experience, Monitoring tools experience, Track record of leading high-impact initiatives, Evaluate proposed work against team goals, Provide critical feedback, Independently implement small to medium sized features, Continuous improvement mindset, Identify process gaps, Propose solutions, Seek out feedback, Experience in a relevant consumer-centric domain, Advise stakeholders on ML application, Strategic Problem Solving ability, Break down vague requirements, Clear Communication skills, Accountability, Take ownership of critical systems, Participate in on-call rotations, Continuous Learning
Nice to Have
Retail, fintech, or utilities experience, Feature Stores experience, Feast experience, Tecton experience, Streaming data technologies knowledge, Kafka knowledge, Pyspark knowledge, Hands-on experience building LLM applications, RAG architectures experience, Vector databases experience
What You'll Do.
Deploy robust ML models
Develop Customer Segmentation
Model long-term customer value
Productionise scalable models
Ensure continuous monitoring
Validate model effectiveness
Measure commercial uplift
Translate statistical outputs
Provide actionable insights
Own "Day 2" operations
Build infrastructure for GenAI
Build infrastructure for RAG
How You'll Work.
Team & Collaboration
Partner with Data Scientists; Collaborate with Data Engineers; Work with Marketing; Work with Product; Work with Commercial; Work with Ops stakeholders; Work empathetically with Data Scientists
Communication Scope
Clear Communication; Influence technical audiences; Influence non-technical audiences
Full Job Description
Hi! We're UW. We’re on a mission to take the headache out of utilities by providing them all in one place. One bill for energy, broadband, mobile and insurance and a whole lot of savings! We’re aiming to double in size as we help more people to stop wasting time and money. Big ambitions, to be delivered by people like you. The challenge For our customers and Partners, UW just needs to work – there when you need it, and invisible when you don’t. Just like flicking a switch. Our proposition to customers is simple, but for our technology teams, the behind-the-scenes complexity is what makes it so interesting. Learn more about life in our Tech teams [here](https://uw.co.uk/about-us/careers/technology). Got your attention? Read on.. We work together. Your team and the people you will work with… We work in small, fully autonomous teams that have real ownership of their products. We use the best tool for the job and constantly look for better. We are seeking a production-focused Machine Learning Engineer to bridge the gap between data science research and scalable, reliable software. In this role, you will partner with Data Scientists to re-architect experimental models (POCs)—such as Next Best Action and Churn Propensity—for production. You will own "Day 2" operations including deployment, latency optimization, and monitoring, while also building the infrastructure for GenAI and RAG applications powering our tools. We Deliver Progress.. What you'll do and how you'll make an impact.. As a Machine Learning Engineer at UW, your responsibilities will include: * Predictive Modelling: Design and deploy robust ML models to solve business challenges, specifically Churn Propensity and Next Best Action (NBA) engines. * Customer Analytics: Develop advanced Customer Segmentation using clustering techniques to tailor services and communications. * Commercial Valuation: Own xLTV and ROI logic, modeling long-term customer value to optimize acquisition and retention spend. * Deployment &
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