Data Scientist, Payments

at Stripe

📍 London, United Kingdom
📍 Dublin, Ireland

EUR 77,200-115,800 per year

MIDDLE

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 5 Data Science @ 5 Experimentation @ 6 Hadoop @ 3 Machine Learning @ 6 Mathematics @ 3 Payments Python @ 5 R @ 5 SQL @ 5 Spark @ 3 Statistics @ 6

Details

Who We Are

Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow revenue, and accelerate new business opportunities. Stripe's mission is to increase the GDP of the internet.

The Data Science team partners with teams across Stripe to build models, data products, and insights that support responsible decision-making and growth. The team works on machine learning, statistical modeling, experimentation, fraud prevention, payment optimization, forecasting, liquidity management, risk quantification, and analytics.

Responsibilities

Partner with Local Payment Methods engineering and product teams.
Use data to understand, grow, and optimize the Local Payment Methods business.
Apply machine learning, statistical modeling, causal inference, optimization, experimentation, and analytics to inform company strategy, products, and user interactions.
Deliver results in collaboration with cross-functional teams.
Manage and deliver multiple projects with a high attention to detail.
Synthesize complex analyses into actionable recommendations.

Requirements

PhD, MSc, or MA with 2 years of data science or quantitative modeling experience, or a BS or BA with 3 years of relevant experience.
Proficiency in SQL and a computing language such as Python or R.
Experience working with cross-functional teams to deliver results.
Ability to communicate results clearly and focus on driving impact.
Strong business acumen.
Proficiency with AI tools to accelerate model development, analysis, and coding.

Preferred Qualifications

Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation.
Experience deploying models in production and adjusting model thresholds to improve performance.
Experience designing, running, and analyzing complex experiments or leveraging causal inference designs.
A builder's mindset and willingness to question assumptions and conventional wisdom.
Experience with distributed tools such as Spark and Hadoop.
A PhD or MSc in a quantitative field such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research.