Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Lead Data Scientist - Treasury Liquidity

Wise
City of London
1 week ago
Create job alert
Company Description

Wise is a global technology company, building the best way to move and manage the world’s money.
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.


More about our mission and what we offer.


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

As part of the team, you’ll be at the forefront of designing, implementing, and refining models that forecast and manage liquidity, and influencing decision-making processes across the organisation. Your mission is to help us have enough cash in the right place at the right time and make sure we keep the liquidity risk under control. You will work closely with cross-functional teams to develop data-driven solutions that enhance our liquidity management and operational efficiency.


Here’s how you’ll be contributing
Liquidity management

  • Work closely with Treasury operations to develop supply and demand forecasts and incorporate them into the 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 backoffice, 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.



Qualifications

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.



Some extra skills that are great (but not essential)

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



Additional Information

For everyone, everywhere. We're people building money without borders — without judgement or prejudice, too. We believe teams are strongest when they are diverse, equitable and inclusive.


We're proud to have a truly international team, and we celebrate our differences. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.


If you want to find out more about what it's like to work at Wise visit Wise.Jobs.


Keep up to date with life at Wise by following us on LinkedIn and Instagram.


#J-18808-Ljbffr

Related Jobs

View all jobs

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist - Remote

Lead Data Scientist - Remote

Lead Data Scientist

Lead Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.