• Designs and develops scalable solutions using AI tools and machine-learning models. • Performs research and testing to develop machine learning algorithms and predictive models. • Utilizes big data computation and storage tools to create prototypes and datasets. • Conducts model training and evaluation. • Integrates, tests, tunes, and monitors solutions. • Participates in model brainstorming conversations with new potential internal customers interested in AI/ML capabilities. • Works alongside internal customers that are requesting AI/ML capabilities to understand overall business objective & impact. • Analyzes use cases of ML algorithms and apply findings to enable business decisions. • Writing moderately complex SQL and Python/R code to extract data from various databases and data sources. • Explores and visualizes data to gain an understanding and verify data quality. • Conduct exploratory data analysis from complex data sources and build key data sets to support operational analysis. • Builds ML/AI models using common methods within R and Python. • Analyzes ML algorithms that could solve a problem and ranks them by success probability. • Analyzes errors of the model and designs strategies to overcome them. • Develop logging, alerting, and mitigation strategies for handling model errors. • Develop AI/ML models to distinguish relevant content or events and recognize patterns. • Designs and develops AI/ML models to predict member outcomes. • Evaluate and design experiments to monitor key model metrics and identify improvement opportunities. • Participate in presentations and communicate research analysis results to non-technical business partners. • Produces fairness review for each model to ensure it's free of bias. • Set up CI/CD pipelines used for model automation. • Model deployment with containers such as Kubernetes and Docker. • Produces clean & readable code that has been properly unit tested and optimized.

Role Description Join one of the UK Government’s largest digital data transformation programmes, helping to centralise Local Authority Land Charge registers into a single Land Registry system. We’re looking for a Data Coordinator to own the relationship with an assigned group of Local Authorities. You’ll guide Local Authorities through the transformation process, lead the analysis of large datasets, and ensure successful data migration. You’ll manage tasks, track progress, and contribute to improving our processes and tools. - Own the relationship with your assigned Local Authorities - Analyse and manage large datasets (100k+ records) - Guide Local Authorities through data transformation - Write and document transformation rules - Quality check and cleanse data - Track progress and manage stakeholder tasks - Contribute to process improvements and team support Qualifications - Exceptional stakeholder management and ability to communicate confidently across all levels internally and externally - Advanced Excel skills, including complex functions and data manipulation - Customer-facing experience, with a professional and approachable communication style - Strong analytical and problem-solving abilities, with a keen eye for data quality - Experience managing data and coordinating processes across multiple workstreams - Working knowledge of SQL or FME (desirable) - Experience with GIS tools and spatial data (highly desirable)

• Define and execute data science strategy aligned to business objectives and KPIs • Lead and mentor data scientists and analysts • Drive workforce transformation and modernization initiatives aligned with business strategy and operating model evolution • Establish modeling, analytics, and governance best practices • Design KPI frameworks for WF optimization, utilization, NPS, revenue and other operational metrics • Build and scale Power BI dashboards and reporting ecosystems • Deliver agentic AI solutions to automate workflows • Translate complex analytics into business insights for executives

• Own ML models end to end - frame the problem, write the design/RFC, build and train the model, ship it to a served endpoint and monitor its quality in production. • Build your model's training pipeline and package it for serving, deploying onto the shared platform Data Engineering builds and operates. • Own model quality, not the platform - you watch drift and performance and decide when to retrain. • Evaluate rigorously - experimental design, statistical validation, drift detection and retraining, champion-challenger evaluation and promotion. • Partner across the org - your model outputs feed the attributes/enrichment layer, payments risk, dashboards and client integrations, you'll collaborate with Data Engineering, backend, product and QA on contracts, deployment and rollout. • Move fast with AI-assisted development - we use it to accelerate implementation and experimentation, the highest-leverage contribution in this role comes from strong problem framing, system design, evaluation rigor and clear technical specifications.