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