Data Scientist, Liquidity Management

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.

About the team

We’re working on making the global financial system programmable. This is one of the largest opportunities for impact in the history of computing, on par with the rise of modern operating systems. Enabling the realization of this opportunity and simultaneously ensuring we optimally manage the liquidity flows from global payment processing and corporate activities, the Liquidity Management team plays a critical role in the company’s financial health.

What you’ll do

We're looking for an experienced Data Scientist to partner with the Liquidity Management team to drive the use of data and modeling to ensure money is in the right place, at the right time and in the right currency. You will help us to build predictive capabilities for user balance and corporate cash flows, develop stress testing models for quantifying liquidity risk and conduct analyses to inform our financial resource needs and business decisions. In doing so, you will work closely with the Liquidity Management, Engineering and Product teams to unlock new product capabilities and implement strategies for reducing costs and increasing capital efficiency.

Responsibilities

• Analyze trends in user level liquidity requirements and corporate cash movements and develop data-driven recommendations to inform business decisions 
• Design and develop stress testing models over different time horizons and equip business stakeholders with insights into drivers of liquidity risk
• Build automated controls and reporting on cash inflows and outflows in the Stripe ecosystem
• Build scalable automation solutions utilizing SQL, Spark, and visualization tools
• Develop statistical and machine learning models to forecast regional liquidity flows related to user balance and corporate cash activities
• Work cross-functionally to unblock and fund new product capabilities
• Collaborate with Engineering teams to ensure that our liquidity management systems are appropriate for the scale and complexity of our business
• Utilize optimization and simulation methods to develop strategies for reducing costs and increasing capital efficiency in managing volume flows in Stripe’s ecosystem
• Shape and influence our data models and instrumentation to generate insights and develop new data products and models
• Work with Product, Engineering and Finance teams to analyze new product opportunities and inform product design and financing requirements

Who you are

The ideal candidate has experience in analytics and statistical modeling for Liquidity Management, thinks creatively about measurement that leads to actionable outcomes, and values rigor in data and modeling.

Minimum requirements

• 5+ years of data science/quantitative modeling experience, including 3+ years experience analyzing large data sets in the financial sector 
• A PhD or MS in a quantitative field (e.g., Quantitative Finance, Economics, Mathematics, Sciences, Engineering, Operations Research, Statistics)
• Strong working knowledge of SQL, R, Python, Matlab, C++, or equivalent.
• Strong understanding of financial industry models and liquidity management systems
• Expertise in statistics and experimental design

Preferred qualifications

• Solid business acumen and experience in synthesizing complex analyses into interpretable content
• Expertise in statistics and experimental design
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
• Ability to communicate results clearly and a focus on driving impact
• A builder's mindset with a willingness to question assumptions and conventional wisdom