Lead Data Scientist - Treasury Liquidity

Job description

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 operate autonomously while working 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.