Data Scientist, Global Growth
The Data Scientist will partner with Global Growth teams to design experiments and optimize Stripe’s self-serve onboarding funnel. The role requires substantial data science or quantitative modeling experience, strong SQL and programming skills, and expertise in experimentation, analytics, or related quantitative methods.
About the job
Responsibilities
- Partner with Global Growth teams to design and ship experiments.
- Identify improvement opportunities across stripe.com and the dashboard to help businesses worldwide get started on 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 inform company strategy, products, and user interactions.
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 drive impact.
- Ability to manage and deliver multiple projects with high 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.
Nice-to-haves
- Hands-on experience in several of the following: 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.
Skills
SQL, Python, R, Machine Learning, Statistics, Optimization, Product Analytics, Causal Inference, Experimentation, Spark, Hadoop, AI Tools
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