Lead Data Scientist - Treasury Liquidity

IVP; City of Westminster; On-site; GBP 60,000 - 80,000; Full time

Job summary

A leading financial technology firm in the City of Westminster is seeking an Operating Liquidity Data Scientist to optimize liquidity models. This position involves deep collaboration with various teams and a focus on data analysis to drive efficiencies. Ideal candidates will have strong Python skills, experience with big data technologies, and a solid understanding of statistics and machine learning. This role is crucial for enhancing liquidity management practices and delivering customer-focused solutions.

Qualifications

• Strong Python knowledge and ability to read code.
• Experience with big data technologies and cloud platforms.
• Strong knowledge in statistics, machine learning, and optimization.

Responsibilities

• Develop supply and demand forecasts and incorporate them into processes.
• Conduct data analysis to support liquidity efficiency initiatives.
• Collaborate to implement models within treasury operations.

Skills

Python

Data analysis

Statistics

Machine Learning

Problem-solving

Tools

Hadoop

Spark

AWS

GCP

Azure

Job description

We are seeking a talented Operating Liquidity Data Scientist with expertise in Liquidity management modelling to join our dynamic Treasury team. This role focuses on driving our models and optimising their impact on our liquidity usage. Your work will have a direct impact on Wise's mission and millions of our customers.

About the Role:

How you'll be contributing:

• Work closely with Treasury operations to develop supply and demand forecasts and incorporate them into real‑time money movement processes across a multi‑region portfolio of products and currencies.
• Conduct rigorous data analysis to support liquidity efficiency initiatives, ensuring a balance between sufficiency and excess.
• Collaborate with engineering teams to implement models within the treasury's operational back‑office, ensuring scalability and operational efficiency.
• Develop bespoke models and analyses in preparation for stress events and new product launches.

Liquidity Risk Modelling and Analysis

• Develop models and infrastructure for understanding liquidity consumption by company's products.
• Partner with product and operational teams to translate complex liquidity risk scenarios into actionable insights for customer‑focused solutions.
• Document and present model results and risk assessments to senior stakeholders, controllers and the Risk team (the second line of defence). Explain complex concepts and propose strategies that align with the company's risk appetite and business objectives.

A bit about you:

• Strong Python knowledge. Ability to read through code.
• Demonstrable experience collaborating with engineers.
• Experience with big data technologies such as Hadoop, Spark, or similar. Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and data warehousing solutions.
• Strong knowledge in at least a few of the following areas: statistics, machine learning, linear algebra, optimisation.
• A strong product mindset with the ability to work in a cross‑functional and cross‑team environment.
• Good communication skills and ability to get the point across to non‑technical individuals.
• Strong problem‑solving skills with the ability to help refine problem statements and figure out how to solve them.

Additional Skills

• Experience in supply/demand modelling and forecasting - could be in supply chain optimisation or liquidity management in a financial company.

About Wise

Wise is a global technology company building the best way to move money around the world. Min fees. Max ease. Full speed. Whether people and businesses are sending money to another country, spending abroad, or making and receiving international payments, Wise is on a mission to make their lives easier and save them money. As part of our team, you will be helping us create an entirely new network for the world's money. For everyone, everywhere. We believe teams are strongest when they are diverse, equitable and inclusive.