Fraud Investigations, Data Analyst
US remote
Stripe
Stripe powers online and in-person payment processing and financial solutions for businesses of all sizes. Accept payments, send payouts, and automate financial processes with a suite of APIs and no-code tools.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. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.
About the team
The Risk Operations team is looking for an experienced fraud analyst to join an industry leading global fraud operations team. This position is responsible for conducting complex data analysis to identify & mitigate large scale distributed fraud attacks, working closely with fraud strategy/detection/data science to implement mitigations, and working collaboratively with fraud stakeholders to expand automated detection of fraudulent merchants. They should have a deep understanding of fraud patterns/typologies, advanced SQL proficiency, and strong analytical abilities.
What you’ll do
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.
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.
The right candidate for this role will have a minimum 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.
Responsibilities
- 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.
- 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
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Banking Classification Clustering Data analysis Data visualization E-commerce Engineering FinTech Fraud risk Python Splunk SQL Statistics Testing Unstructured data
More jobs like this
Explore more AI, ML, Data Science career opportunities
Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.
- Open Lead Data Analyst jobs
- Open Senior Business Intelligence Analyst jobs
- Open MLOps Engineer jobs
- Open Data Manager jobs
- Open Data Science Manager jobs
- Open Principal Data Engineer jobs
- Open Data Engineer II jobs
- Open Sr Data Engineer jobs
- Open Power BI Developer jobs
- Open Product Data Analyst jobs
- Open Business Intelligence Developer jobs
- Open Data Scientist II jobs
- Open Junior Data Scientist jobs
- Open Data Analytics Engineer jobs
- Open Business Data Analyst jobs
- Open Sr. Data Scientist jobs
- Open Senior Data Architect jobs
- Open Data Analyst Intern jobs
- Open Big Data Engineer jobs
- Open Manager, Data Engineering jobs
- Open Junior Data Engineer jobs
- Open Data Quality Analyst jobs
- Open Data Product Manager jobs
- Open Principal Data Scientist jobs
- Open Azure Data Engineer jobs
- Open GCP-related jobs
- Open Data quality-related jobs
- Open Business Intelligence-related jobs
- Open Java-related jobs
- Open ML models-related jobs
- Open Data management-related jobs
- Open Privacy-related jobs
- Open Data visualization-related jobs
- Open Finance-related jobs
- Open Deep Learning-related jobs
- Open PhD-related jobs
- Open APIs-related jobs
- Open TensorFlow-related jobs
- Open PyTorch-related jobs
- Open NLP-related jobs
- Open Consulting-related jobs
- Open Snowflake-related jobs
- Open CI/CD-related jobs
- Open LLMs-related jobs
- Open Generative AI-related jobs
- Open Kubernetes-related jobs
- Open Data governance-related jobs
- Open Hadoop-related jobs
- Open Airflow-related jobs
- Open Docker-related jobs