In this role you will work in the Customer Success team, within the Technical Operations function responsible for the ongoing monitoring end technical support of projects and deployment of the OneView solution and deployment of the OneView platform. As a Junior ML Engineer here, your core work is monitoring, training, evaluating, and productionising machine learning models on complex, multi‑source datasets from local authorities. You’ll work closely with our Customer Success team, Technical Operations colleagues within the team and internal teams to deliver data engineering, data science, platform configuration, and data visualisation. You’ll own technical components of deployments, delivering high‑quality work on time and solving problems independently. You’ll also help optimise how we work, supporting our ability to scale. You’ll collaborate with Customer Success Managers, who oversee the non‑technical elements of delivery, as well as other key teams including Information Governance (privacy and security), Platform Delivery (core product development), and Quality Assurance (testing and validation). Key Responsibilities

Machine learning engineering – Demonstrate previous experience and capability to optimise predictive models using advanced architectures such as gradient‑boosted trees, temporal models and embedding‑based models. Data science - Configure existing predictive models to meet the client’s needs, apply descriptive analytics techniques to extract meaningful insight from client data, build simple proxy predictive models to demonstrate value early on. Cohort building - Build preventative cohorts, test and adapt these with the client to optimise accuracy & efficacy Dashboards – Design, build and adapt dashboards to meet the client’s needs and tell them what they need to know in a clear, intuitive way Platform evolution – Feed innovations back into the core platform

Technical project work

Own and deliver the technical components of deployments Prioritise effectively, flag and resolve blockers early, and collaborate well across teams

Problem solving and storytelling

Use your analytical skills to extract meaningful insights from data Translate complex analysis into clear, actionable recommendations for clients

Communication and stakeholder support

Support client upskilling by sharing knowledge and documentation Work closely with Customer Success colleagues to deliver successful solutions and measure the impact Identify opportunities to improve how we deliver as a team and share best practices

What are we looking for? We’d love to hear from you if you have: Requirements

A degree in a quantitative or technical field such as Machine Learning, Data Science, Computer Science, Maths, Engineering etc Previous experience in data and analytics, including exposure to data engineering, data science and data visualisation: Proficiency in Python and/or SQL for data wrangling and analysis Previous data science knowledge, with experience in machine learning techniques Experience with relational databases (e.g., SQL Server or Oracle) Familiarity with tools like Power BI or Tableau to build clear, insightful dashboards Experience writing clean, testable, traceable code using good QA practices Experience delivering technical work, with strong attention to detail and ability to manage your own deadlines Strong analytical and problem‑solving skills, with experience in techniques such as regression, correlation analysis, or EDA Clear communication skills, both written and verbal. Able to explain technical work to non‑technical audiences A continuous improvement mindset, you look for opportunities to improve our tools, documentation, or ways of working A passion for social impact – you’re excited about what we’re doing and driven to help Xantura succeed

Bonus Points if you have:

Experience working with the public sector (local or central government) or as a vendor/consultant to public sector clients Familiarity with privacy, security, and information governance in data projects Familiarity with cloud tools and services such as Azure Data Factory, Azure ML or AWS equivalents

Location – This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1‑2 days per week. Some travel is also required for on‑site client engagements as needed. #J-18808-Ljbffr