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Wise

Low-cost international money transfers with API

Quantitative Researcher / Developer - Data Science, Treasury FX

Quantitative Research +1Confirmed live in the last 24 hours
Full-timeLondon, UK£90.5k - £127k /yr

About the Job

Requirements

  • At least 4 years of relevant quantitative or engineering experience with strong Python development skills.
  • A quantitative background in mathematics, physics, engineering, or finance, with the ability to read models and reason about correctness.
  • Experience taking quantitative work into production and owning its ongoing validation, monitoring, and improvement.
  • Clear communication and the ability to work cross-functionally with quants, analysts, traders, product managers, and engineers.

Responsibilities

  • Own a primary focus in either production quantitative engineering or applied quantitative modeling.
  • Implement quantitative work in Python from research or prototype through reliable production use.
  • Validate, backtest, and monitor model and service performance against realized outcomes.
  • Handle deployment, incident response, root-cause analysis, and continuous improvement with stakeholders.
  • Maintain shared quantitative libraries used across multiple services.
  • Build and operate continuous integration and continuous delivery pipelines, deployments, and operational excellence practices.
  • Monitor, alert, and maintain reliability for real-time pricing and risk systems.
  • Manage market data and onboard new pipelines.
  • Design new quantitative infrastructure and systems with the engineering team.
  • Develop backtesting frameworks, model validation, and risk modeling alongside the Risk team, including value-at-risk, stress testing, and scenario analysis.
  • Model customer behavior and contribute to pricing strategy and product development.
  • Collaborate with product teams to translate quantitative insights into customer-facing decisions.

Desired Qualifications

  • Familiarity with foreign exchange or financial markets.
  • Experience with term structure modeling, stochastic calculus, or Monte Carlo methods.
  • Experience with interest rate curve bootstrapping.
  • Algorithmic execution experience.
  • Data lake or warehouse experience with technologies such as Snowflake, Iceberg, or Spark.
  • For the Researcher track, strong experience in quantitative research or financial modeling, with depth in pricing, forecasting, risk, or trading.
  • For the Researcher track, ability to translate an open-ended financial problem into a quantitative model, choose appropriate statistical or numerical methods, and explain assumptions and limitations.
  • For the Researcher track, experience designing backtests and out-of-sample validation while accounting for data leakage, transaction costs, and changing market conditions.
  • For the Developer track, experience with streaming systems, real-time data pipelines, or event-driven architectures such as Kafka, Flink, or Redis.
  • For the Developer track, experience testing, monitoring, and debugging complex systems under load.
  • For the Developer track, strong experience with microservices, databases, and production infrastructure.

About the Company

Wise

Low-cost international money transfers with API

Wise enables international money transfers with lower fees by using a peer-to-peer model to match transfers across borders. It offers a multi-currency account for holding and managing money in different currencies and provides an API for businesses to integrate Wise transfers into their platforms. Unlike traditional banks that charge higher fees and poor exchange rates, Wise uses transparent, small-per-transaction fees and real-time exchange rates to reduce costs. Its approach differentiates it from competitors by emphasizing simplicity, cost transparency, and a broad API for business integration. The goal is to make cross-border payments cheaper, faster, and more transparent for individuals and businesses around the world.

Company size
10,001+
Company stage
IPO
Headquarters
London, United Kingdom
Founded
2011

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