About Fable Data: Fable Data is a global consumer transaction data company. We aggregate anonymised consumer data from financial services businesses which we then enrich and productise to deliver high value data products to some of the world’s leading retailers, investment managers, technology companies, governments, and advertising firms. Our data provides a near real-time view of the consumer economy, offering powerful insights into consumer behaviour, retailer performance and broader macroeconomic trends. About the Role: This is a newly created hybrid role combining MLOps, data engineering and data science. While not a deep platform engineering role, it gives you real hands-on exposure to Fable's production pipelines before progressing into more specialised data science and product work. You'll start by shadowing platform BAU work to get under the hood of how Fable's pipelines and infrastructure actually run. The first few months are deliberately structured around learning the platform fundamentals which includes projects in cluster migration, tagging model changes, library and runtime support, and regression testing before moving into MLOps tasks proper and, typically within 9-12 months, into product-facing data science work. You'll work closely with the Product & Data Platform Lead's team on platform and pipeline delivery, and collaborate with the Infrastructure Lead on data engineering and AI-related projects. As you build platform fluency, the role is designed to flex towards product builds and partner onboarding, working alongside a senior product engineer as they shift more of their time towards AI and product work. Your line manager remains the Product & Data Platform Lead throughout, who will coordinate priorities across these collaborations. What good looks like: by month 4, you're comfortable navigating the platform and contributing to BAU tasks independently; by month 7, you're taking on MLOps work with light supervision; by month 10-12, you're contributing to product-facing data science work alongside the senior product engineer. About You: - Comfortable starting close to the platform: you're happy learning pipelines, infrastructure and BAU workflows in depth before moving into more specialised work. - A natural bridge-builder, equally comfortable in an engineering conversation about pipeline resilience and a data science conversation about model behaviour. - Genuinely interested in MLOps — deployment, monitoring, regression testing and keeping models healthy in production — as a route into broader data science and product work. - Strong attention to detail and comfortable working with ambiguity in a fast-paced scale-up environment. What you’ll do: - Shadow platform BAU tasks alongside the Product & Data Platform Lead's team to build a working understanding of Fable's pipelines and infrastructure. - Support cluster migration work and tagging model changes. - Contribute to library and runtime support, and regression testing across the platform. - Take on core MLOps tasks, supporting model versioning, deployment and monitoring as you build platform fluency. - Progress into more specialised data science and product-facing work, paired with a senior product engineer, as the core team for upcoming product builds and partner onboarding. - Contribute to future platform and AI-related projects including patching framework improvements, runtime changes, and library updates working with the Platform/Infrastructure Lead. - Help productionise data science work, bridging the Data Science Lead's team and the Product & Data Platform Lead's team as models move from notebook to deployment. - Support monitoring of in-life ML models, helping flag and triage production issues as they arise. Skills / knowledge: Essential - 2+ years' experience working with large datasets in a professional setting, spanning data engineering and/or data science. - Strong fundamentals in Python and SQL (Spark) for data engineering, analytics, and ML support tasks. - Comfortable working with Git-based development workflows, including pull requests, branching strategies, and collaborative code reviews. - Working knowledge of data quality controls, schema evolution, and data governance best practices. - Comfortable with data pipeline fundamentals: schema validation, data quality checks, and troubleshooting pipeline issues. - Hands-on experience with Spark/Databricks and a cloud platform (Azure preferred). - Comfortable reading and reasoning about Jupyter notebooks, pandas/numpy code, and at least one ML library (e.g. scikit-learn) at a conceptual level. - Conceptual understanding of the ML lifecycle (train/test splits, evaluation metrics, overfitting) and of model versioning/experiment tracking tools (e.g. MLflow, Weights & Biases). - Exposure to CI/CD concepts — able to read and reason about a .yml config and understand what a pipeline does, even without designing pipelines unsupervised. Desirable - Experience supporting deployment and monitoring of ML models in production (MLOps). - Experience with regression testing, library/runtime upgrades, or cluster migration work. - Knowledge of or experience with Snowflake and data warehousing/data lake concepts. - Familiarity with classification, time series, and/or natural language processing. - Curiosity about how commercial and client-facing requirements translate into data product decisions. *We regret we are currently unable to provide visa sponsorship; please only apply if you have the right to work in the UK.* Why Fable At Fable, we believe in continuous improvement and shared responsibility. You’ll join a collaborative team that empowers you to take ownership of your work, experiment, and grow your skills. This is a unique opportunity to combine MLOps, data engineering and data science within a hybrid role, and have your work seen by some of the most influential organisations in the world. As your scope grows into product-facing data science work, compensation is reviewed accordingly as part of our regular pay review cycle.