Senior Data Scientist – Fintech (Credit and Fraud)

We are on the lookout for a Senior Data Scientist to join the Fintech Data - Credit Risk & Fraud tribe on our journey to always deliver amazing experiences.
Be part of building the financial backbone of Delivery Hero. You’ll develop products that empower millions of customers and merchants, from seamless payments to innovative financial solutions like wallets and credit. Your work will support our path to profitability by creating financial flexibility for users and enabling smooth transactions across our markets.
The Fintech Data Science team sits at the center of how Delivery Hero extends credit and protects the business from fraud losses, spanning underwriting, portfolio risk management, and fraud prevention across our products. We build the models and decision systems behind credit approval, limit management, and fraud detection, enabling the business to grow lending responsibly while keeping losses, chargebacks, and financial crime exposure under control. We're looking for a Senior Data Scientist who can own these problems end-to-end from framing an ambiguous risk question, through building and validating the model, to getting it live in a real-time decision engine and defending it in front of a risk committee. This role combines deep statistical and machine learning expertise with practical credit and fraud domain knowledge, and the judgment to operate under constraints a pure modeling exercise doesn't have.
We expect you to work with AI. Our team uses coding agents and LLM tools every day to build pipelines, explore data, write and review code, and write tests and documentation. We want people who use these tools to work faster, and who check the output instead of trusting it.
In this role you will,
Own problems end to end, from an unclear business question through data, modelling, deployment, monitoring, and adoption.
Build and ship models: fraud and anomaly detection, real-time transaction decisioning, acquisition scorecards, and behavioural limit management.
Make decisions and explain them. Give Risk, Compliance, Finance, Payments, and leadership a clear recommendation they can act on.
Work with Engineering closely on reliability, owning DS lifecycle including deployment.
Build the data foundations: data marts and pipelines that produce features and training datasets.
Drive experimentation towards model rollout and monitoring.
Use AI tools to work faster, and review what they produce.
A track record of owning models end to end, from an unclear business question through build, validation, deployment, and real use by the business.
Full-stack breadth: SQL and data analysis (e.g. BigQuery), orchestration and scheduled feature and training-data pipelines (e.g. Airflow), modelling, deployment (e.g. Metaflow, Vertex AI), and production monitoring. Strong Python, testing, and CI/CD.
Fluency with AI tooling. You build with coding agents and LLMs every day, automate your own routine work, and spend LLM budget sensibly: the right model for the task, tight context, no tokens burned on what a script should do. And you catch what the output gets wrong.
Independence and speed. You pick the approach, cut the scope, and ship, then explain why.
Judgment under ambiguity. You can make a defensible recommendation when trade-offs have no single right answer: false positives vs. fraud caught, loss vs. approval rate.
Clear communication. You can explain modelling trade-offs to non-technical stakeholders and hold your position when challenged.
Nice to have
Experience in fraud, credit risk, payments, or fintech.
Real-time or streaming decisioning systems.
Building and deploying deep learning models in production.
Building LLM- or agent-based solutions in production.

Employment type
Full-time

Work arrangement
Hybrid