Data Scientist, Global Growth

at Stripe

📍 Singapore, Singapore

SGD 172,000-258,000 per year

SENIOR

✅ 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 @ 6 Data Science @ 7 Experimentation @ 7 Hadoop @ 4 Machine Learning @ 7 Mathematics @ 6 Payments Python @ 6 R @ 6 SQL @ 6 Spark @ 4 Statistics @ 7

Details

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. Stripe’s mission is to increase the GDP of the internet.

About the Team

The Data Science team partners with teams across Stripe to provide the models, data products, and insights needed to make decisions and grow responsibly. The team analyzes data, builds machine learning and statistical models, and runs experiments to drive impact across products, business operations, and go-to-market activities.

Responsibilities

Partner with Global Growth teams to design and ship experiments.
Identify improvement opportunities across stripe.com and the Stripe Dashboard to help businesses worldwide get started with Stripe.
Understand, grow, and optimize the self-serve user funnel.
Improve the quality of the global user onboarding experience.
Apply machine learning, statistical modeling, causal inference, optimization, experimentation, and analytics to support company strategy, products, and user interactions.
Work with cross-functional teams to deliver results and drive business impact.

Requirements

Minimum Requirements

Bachelor’s degree plus 8 years, Master’s degree plus 6 years, or PhD plus 3 years of data science or quantitative modeling 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.
Demonstrated ability to manage and deliver multiple projects with strong attention to detail.
Strong business acumen and experience synthesizing complex analyses into actionable recommendations.
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.
PhD or MSc in a quantitative field such as Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, or Operations Research.