Stripe is hiring a

Fraud Investigations

Job Overview

  • Posted 4 weeks ago
  • Full Time
  • USA
  • 65000

Roles & Responsibilities

Did you know that only around 4% of the world’s GDP comes from internet commerce? At Stripe, we believe that this represents a future with almost limitless potential for innovation, creativity and global prosperity. While the promise of a global online economy is palpable, it doesn’t come without significant risk. Each day, bad actors disrupt the trust and safety of the internet and increase the barrier of entry for online businesses. Before we can fully realize the potential of a global internet economy, we must first address the burgeoning problem of fraud.Β 

We are looking for someone passionate about fighting fraud, identifying new trends/typologies, conducting complex data analysis, and has a strong desire to work collaboratively with peers and partners in the fraud space. This position works closely with cross-functional stakeholders across product, engineering, data science, and operations to identify and mitigate risk from complex, distributed merchant and transaction fraud attacks.Β 

The right candidate for this role will have aΒ minimumΒ of three years experience conducting complex data analysis using SQL, preferably within the fraud space across ecommerce or payments. Candidates should also have experience working closely with engineering and data science teams to drive automated fraud detection and demonstrate a deep understanding of fraud typologies, controls, and ability to mitigate fraud risk.

Conduct advanced data analysis of structured and unstructured data sets to proactively identify emerging complex fraud attacks impacting Stripe and its users.
Investigate, conduct root cause analysis, and deploy remediations to prevent future complex and distributed fraud attacks encompassing merchant fraud, transaction fraud, card testing, and local payment methods.Β 
Investigate and take action against anomalous clusters of merchants based on account activity, processing volume, or other risk indicators while minimizing negative impacts to Stripe users.
Work in lockstep with engineering and data science teams to enhance automated detection and actioning of fraudulent accounts to minimize risks to Stripe and partner ecosystems.
Respond to high priority incidents involving complex fraud schemes to quickly mitigate exposure to Stripe, its users, and financial partners.
Utilize analytics to identify & implement initiatives to automate manual processes and workload across the organization.
Create visualizations, dashboards, and queries to drive visibility and oversight into organization impact, performance, and loss risks.
Utilize Stripe tools & systems to enable systematic actioning of fraudulent merchants, maintaining an extremely high level of accuracy to prevent negative user experience.
Who you are
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.

Minimum requirements
A minimum of three years of experience conducting advanced data analysis.
Advanced level proficiency in SQL
Experience working closely with modeling, data science, and intelligence stakeholders to implement automatic & scaled controls & processes.
Experience creating data visualizations and dashboards & presenting findings to technical and non-technical audiences, including senior leadership.
You have the ability to drive execution on projects working in a heavily cross-functional environment.
Creativity, a team-focused mentality, and effective problem solving skills.
The ability and desire to question the status quo.
The ability to approach challenges from a user perspective while being pragmatic & solutions oriented.

Preferred qualifications
Proficiency in Splunk, Python, and data visualization tools.
Advanced data analysis in the fraud and risk space, preferably in payments, fintech, or banking.
Undergraduate or advanced degree in analytics, data science, or statistics
Experience with clustering, classification, & link analysis
Experience working in fast-paced and rapidly changing environments

Skills Required

  • Machine Learning
  • Python

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