Senior Machine Learning Engineer at Utility Warehouse

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

30 Jun, 26

Salary

0.0

Posted On

01 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Software Engineering, Docker, Kubernetes, MLOps, Model Registries, Monitoring Tools, Feature Stores, Streaming Data Technologies, Customer Analytics, Predictive Modelling, A/B Testing, Stakeholder Partnership, Data Engineering, Churn Propensity

Industry

Utilities

Description

Company 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. Got your attention? Read on.. We put people first.. it's all about you! 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. Job Description 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 & Ops: Collaborate with Data Engineers to productionise scalable models, ensuring continuous monitoring for drift and performance. Experimentation: Design and analyse A/B tests to validate model effectiveness and measure commercial uplift. Stakeholder Partnership: Translate complex statistical outputs into actionable insights for Marketing, Product, Commercial and Ops stakeholders. Qualifications We put people first. It’s all about you.. Technical Mastery: Production ML Experience: Proven experience deploying Machine Learning models into high-traffic production environments (retail, fintech, or utilities experience is a plus). Tech Stack: Strong proficiency in Python and software engineering best practices (unit testing, modular code, Git). Experience with containerization (Docker, Kubernetes) is essential. MLOps Tooling: Experience with model registries and monitoring tools (e.g., MLflow, Grafana). Desirables: Experience with Feature Stores (e.g., Feast, Tecton). Knowledge of streaming data technologies (Kafka, Pyspark). Hands-on experience building or deploying LLM-based applications, specifically working with RAG architectures and vector databases. Impact & Scope: You have a track record of leading high-impact initiatives that align with company strategy. You can evaluate proposed work against team goals and provide critical feedback to ensure value delivery. Planning & Delivery: You are capable of independently implementing small to medium sized features through to completion. Operational Excellence: Continuous improvement mindset: Identify process gaps and proactively propose solutions, seeking out feedback from your team. Business & Domain Knowledge: Experience in working in a relevant consumer-centric domain Can advise stakeholders on how Machine Learning Engineering can be applied to solve business problems Leadership & Culture: Collaboration: A "Software Engineering mindset" with the ability to work empathetically with Data Scientists, understanding their workflows while enforcing production standards. Skills / Competencies Strategic Problem Solving: Ability to break down vague, high-level business requirements into concrete, scalable technical architectures. Clear Communication: Excellent verbal and written skills, with the ability to influence technical and non-technical audiences. Accountability: Willingness to take ownership of critical systems and participate in on-call rotations. Continuous Learning: Proactively seeking out the latest industry trends and introducing relevant innovations to the team. Don’t worry if you don’t have the whole list. If you feel you have most of it and can learn the rest pretty quickly then please don’t hesitate to apply. Overall we are looking for imaginative and pragmatic problem-solvers who want to help make a positive impact with data at UW. Please note we cannot offer visa sponsorship now or in the future to work at UW. Additional Information We’ve got big ambitions so there’s going to be plenty of challenges. There are also a lot of benefits: An industry benchmarked salary. We’ll share it during your first conversation. Discretionary performance bonus Flexible remote working with the option to visit our London hubs for team meetings and company events. You'll have access to a range of hot desking spaces around the UK Electric Car salary sacrifice scheme through Tusker Discount on our services and you get our coveted Cashback Card for free. You’ll also get access to 100s of rewards and discounts through Perkbox A matched contribution pension scheme and life assurance up to 4x your salary. You can also access free mortgage advice and a financial wellbeing tool. Family-friendly policies, designed to help you and your family thrive. Discounted private health insurance, access to an Employee Assistance line and a free Virtual GP. Our wellbeing app Unmind supports your mental health. Belonging groups that help UW shape an even more inclusive future. A commitment to helping you develop your career journey through learning, coaching and new experiences Not sure you meet all the requirements? Let us decide! Research shows that women and members of other underrepresented groups tend not to apply for jobs if they think they may not meet every qualification, when in fact they often do. We provide equal opportunities, a diverse and inclusive work environment, and fairness for everyone. You are welcome to apply no matter your age, disability, gender, marriage or civil partnership status, pregnancy and maternity status, race, religion or belief, or sexual orientation. Please don’t be afraid to ask about what we can do to support your needs. All requests will be carefully and fairly considered. Please note, if you are successful and offered a role at UW, you will be subject to a background check. Where checks are unsatisfactory or incomplete and/or a failure to reveal information relating to convictions that you are required to identify as part of the background checks, could lead to withdrawal of an offer of employment. Employment Status: Full-Time

Responsibilities

As a Senior Machine Learning Engineer, you will design and deploy robust ML models to address business challenges and collaborate with Data Engineers to ensure models are production-ready. You will also analyze A/B tests to validate model effectiveness and translate complex statistical outputs into actionable insights for various stakeholders.