Data Scientist

We're looking for a hands-on Data Scientist / MLE to help us build and deploy intelligent data systems. You'll work closely with the Head of Machine Learning to clean, analyse, and model data, and help operationalise models and customer datasets in production. Prior experience working in payment fraud or the payments domain is a must; you should already be comfortable with the data, risk signals, and problem patterns that come from fraud and transactional environments.

Responsibilities:

• Perform data analysis, feature engineering, and exploratory insights across business datasets.
• Build and deploy ML models (classification, regression, ranking, anomaly detection, etc. ).
• Fine-tune and integrate LLMs (OpenAI, Hugging Face, LangChain, RAG pipelines).
• Support model deployment in cloud / API environments (Docker, GCP).
• Create dashboards, reports, and validation pipelines for model and data monitoring.
• Collaborate with engineering on scalable data workflows and inference pipelines.

Requirements:

• Hands-on experience in payment fraud or the payments domain is required, including exposure to transactional risk signals, fraud detection, or related risk-modelling work.
• Knowledge of Python (NumPy, Pandas, Scikit-learn, TensorFlow or PyTorch).
• Experience with LLM frameworks (Hugging Face, LangChain, OpenAI API).
• Basic knowledge of MLOps (Docker, CI/CD, API serving, Git).
• Proficiency in SQL / NoSQL data querying; familiarity with cloud data environments.
• Strong analytical mindset; comfortable with statistics and data visualisation (e. g., Plotly, Seaborn).

Nice to Have:

• Exposure to RAG, prompt-tuning, or lightweight model fine-tuning.
• Understanding of model monitoring and data drift detection.
• Understanding of Deep Learning and Graphical Data Structures.