Data Scientist, EMEA

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.

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We’re looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g. understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between.

We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

We’re looking for a Data Scientist to partner with the Product, Finance, Payments, Security, Risk, Growth and Go-to-Market teams. You’ll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As a Data Scientist as Stripe, it’s our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.

We’re looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

• 3-8+ years of data science/quantitative modeling experience
• Proficiency in SQL and a computing language such as Python or R
• Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and/or experimentation
• Experience in working with cross-functional teams to deliver results
• Ability to communicate results clearly and a focus on driving impact
• A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
• Solid business acumen and experience in synthesizing complex analyses into actionable recommendations
• A builder's mindset with a willingness to question assumptions and conventional wisdom

• Experience deploying models in production and adjusting model thresholds to improve performance
• Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
• Experience with distributed tools such as Spark, Hadoop, etc.
• A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)

Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in our Bucharest, Romania site have an 80% in-office expectation, and those in Stripe Delivery Center roles in Mexico City, Mexico and Bengaluru, India work 100% from the office.

Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other,…