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About the Role

This newly created hybrid role combines MLOps, data engineering, and data science. It offers hands-on exposure to production pipelines before progressing into specialized data science and product work. You will learn platform fundamentals, including cluster migration, tagging model changes, library and runtime support, and regression testing, before moving into MLOps tasks and eventually product-facing data science work. You will collaborate with the Product & Data Platform Lead's team and the Infrastructure Lead on various projects, with the role designed to flex towards product builds and partner onboarding.

Responsibilities

Shadow platform BAU tasks 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.
Progress into specialized data science and product-facing work, paired with a senior product engineer, for upcoming product builds and partner onboarding.
Contribute to future platform and AI-related projects including patching framework improvements, runtime changes, and library updates.
Help productionize data science work, bridging the Data Science Lead's team and the Product & Data Platform Lead's team.
Support monitoring of in-life ML models, helping flag and triage production issues.

Requirements

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.
Experience supporting deployment and monitoring of ML models in production (MLOps) is desirable.
Experience with regression testing, library/runtime upgrades, or cluster migration work is desirable.
Knowledge of or experience with Snowflake and data warehousing/data lake concepts is desirable.
Familiarity with classification, time series, and/or natural language processing is desirable.
Curiosity about how commercial and client-facing requirements translate into data product decisions is desirable.
Must have the right to work in the UK.

Skills

Python
SQL
Spark
Git
Data Quality Controls
Schema Evolution
Data Governance
Data Pipeline Fundamentals
Spark/Databricks
Cloud Platform (Azure preferred)
Jupyter Notebooks
Pandas
NumPy
ML Libraries (e.g. scikit-learn)
ML Lifecycle Concepts
Model Versioning/Experiment Tracking Tools (e.g. MLflow, Weights & Biases)
CI/CD Concepts

Location

Work Type

Hybrid

Experience Level

2+ years

About the Company

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.
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.

Equal Opportunity

We regret we are currently unable to provide visa sponsorship; please only apply if you have the right to work in the UK.