Staff Data Analyst

Stripe

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Toronto, Ontario, CanadaAtlanta, GAChicago, IL
Salary: $179,000–$268,400 / yr
Posted: 150 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $171k

This role $224k

$110k $285k

below market most similar roles pay here above market

This role pays more than 83% of similar roles. Most pay $126,750–$214,500 — the blue band above. At the midpoint, this role pays about $224k versus about $171k for comparable roles.

Based on 240 similar postings.

Employer

About Stripe

Stripe is a financial infrastructure platform for internet businesses, providing payment processing, billing, fraud prevention, and banking-as-a-service APIs to businesses of all sizes globally. Industry: Payments Infrastructure & Financial Technology

Stripe currently has 140 open roles on FindRole.

Listed pay typically runs $203,600–$285,600 across 137 roles with salary data.

Most-posted roles

View all roles at Stripe

At a glance

TL;DR · Staff Data Analyst

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The Staff Data Analyst joins the Risk Data Science team to drive the data strategy for risk as a product offering. This role involves defining metrics, data products, and analytical frameworks to support risk capabilities for platforms and connected accounts. You will own the definition and reliability of north star and operational metrics, building the infrastructure and scalable pipelines required to maintain accurate insights. Key responsibilities include evolving the risk experimentation strategy to evaluate policy changes and mentoring other analysts on framing ambiguous problems. The role requires deep expertise in SQL, proficiency in Python, and experience with AI tools. You must demonstrate the ability to build operational data products and design measurement strategies for complex systems like risk policies and marketplace dynamics.

What does a Data Analyst earn?

Median $120900 from 80 postings across 23 companies.

See salary data

Skills

SQL Python Data Analytics Data Science A/B testing Causal Inference Metrics Frameworks Dashboards AI Tools Data Strategy Experimentation Frameworks

What you'll do

Define the metrics, data products, and analytical frameworks for risk capabilities offered as a product.
Partner with Product, Engineering, and Risk leadership to align data investments with the product roadmap.
Own the definition, reliability, and visibility of north star and operational risk metrics.
Build and maintain scalable data pipelines and infrastructure to ensure metric accuracy and reduce maintenance costs.
Evolve the risk experimentation strategy by defining test parameters and measurement methodologies.
Evaluate the impact of changes to risk policies, merchant journeys, and risk models across diverse populations.
Mentor data analysts on framing ambiguous problems, structuring analyses, and communicating findings to stakeholders.

What we're looking for

10+ years in Data Analytics, Data Science, or related roles.
Track record of defining and driving data strategy across multiple teams.
Experience designing experimentation frameworks or measurement strategies for complex, multi-variant systems.
Deep expertise in SQL and proficiency in Python.
Ability to translate ambiguous business problems into structured analytical approaches and communicate findings to executive stakeholders.
Experience building and scaling data products like metrics frameworks, pipelines, and dashboards as operational infrastructure.
Demonstrated ability to influence without authority across engineering, product, and business teams.
Proficiency with AI tools to accelerate model development, analysis, and coding.
Master’s degree in Mathematics, Statistics, Economics, Engineering, or a related technical field (preferred).
Experience in risk, trust & safety, or related domains and understanding of risk in the Fintech space (preferred).
Experience building data for platform/product offerings where data is part of the product surface (preferred).
Familiarity with causal inference and A/B testing in non-standard environments (preferred).

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