Data Scientist

Meniga is a leading hyper-personalisation banking platform, our vision is to enable banking that is genuinely personal, proactive, and valuable to every customer. We give banks the data infrastructure to make digital banking genuinely personal, proactive and valuable, not generic. Today we enrich 45 million transactions a day and serve 100+ million banking customers across 165 banks in 30+ countries. We are a global leader in transaction enrichment, AI-powered insights and hyper-personalisation for large financial institutions, a multiple Finovate ''Best of Show'' winner, and featured on CNBC's 2025 list of top UK fintechs. We're a global team with offices in London, Reykjavik, Warsaw, and Cairo.

We are hiring a Data Scientist to work on the science at the core of our platform. Our enrichment engine turns raw, messy transaction data into clean merchant, category and location information. Our intelligence layer builds on those signals to understand each customer's financial life: the behavioural features, scores and segments that power personalisation, CRM, advisory and AI agents for 100+ million banking customers. Your focus may sit on enrichment, on customer intelligence, or across both.

This is high-impact, high-trust work: banks run what you build in production and defend it to auditors and regulators, so everything you ship must be explainable, documented and stable. Day to day you will work in Python, SQL and dbt, with an event warehouse (ClickHouse or equivalent) and Airflow pipelines, on real-world banking data and synthetic datasets used for QA and demos, alongside senior data scientists who set the standards you'll work within.

Key Responsibilities

• Support Transaction and Merchant Enrichment: Help improve the classification and merchant-matching models behind our enrichment engine, and contribute to the quality, coverage and confidence metrics that let banks trust each merchant, category and location signal.
• Build Behavioural Features and Predictive Models: Turn banking event streams into meaningful financial signals - income stability, spend volatility, liquidity, balance trajectory - that are reproducible, documented and ready to use in models and rules, working from specs and frameworks set by senior team members.
• Help Ship Scores Banks Can Defend: Contribute to financial-health, churn and propensity scores and behavioural segments. Every score ships with a clear definition, validation and drift checks, and an explanation a risk or compliance stakeholder can read.
• Apply the Statistical Building Blocks: Banks configure their own rules and metrics on top of these signals. You'll implement and test how functions behave on sparse or messy data, flagging where calibration or explainability breaks down.

Requirements

• Minimum 2-3 years experience as a data scientist
• Experience in banking, lending, cards, wealth or fintech is a strong plus, especially work shipped on transactional data rather than only clickstream
• You can take raw txn/balance/event tables and, with guidance, produce a documented feature or a validated score
• Solid Python (Pandas) + SQL; you write analysis others can rerun. Event/analytical warehouse (ClickHouse or equivalent). Pipelines (Airflow).
  
• Production ML with model and drift monitoring (for example MLflow, feature store, drift)
• Developing statistical judgment: you can apply and explain a chosen method, and are open to feedback on when a rule or simple metric beats a model
• Some exposure to working under audit/explainability requirements - definitions, lineage, avoiding silent leakage - or a clear understanding of why these matter
• You can communicate with product, CRM, and engineering without over-relying on jargon
  
• AI-native - you use tools like Cursor and Claude Code as a natural part of how you work
• Hybrid - 2 days in office, 3 remote
• Full English language proficiency

Nice-to-have

• dbt / Airflow / Spark in anger, not just on a CV
• Real-time or event-driven scoring
• Fraud, credit risk, or financial-health models
• Personalization / next-best-action / marketing decisioning
• Synthetic data for testing (personas, edge cases) - useful here, not a gate
  

What We Offer
Health and Benefits: Private healthcare, fitness allowance and leave benefits.
Supportive Work Environment: Work-life balance, hybrid working, meal allowance, team-building events and reimbursement for internet/phone subscriptions.
Growth Opportunities: A front-row seat in a scaling fintech, international projects and career advancement.
Financial Rewards: Competitive salary