Pipl is an AI company that helps global enterprises make better fraud decisions. We're built on more than 20 years of identity data and Elephant, the industry's only large risk model trained on payment fraud.
Pipl's three products give enterprise teams modular access to the intelligence they need across payment and transaction ecosystems, where the cost of getting it wrong is highest.
Pipl Trust brings AI-native risk decisioning into payment workflows, with Elephant resolving identity across behavioral, device, and network signals in real time. Pipl Search connects identity intelligence for investigations and background checks, drawing on our global identity graph. Pipl Elements delivers phone and email signals that strengthen existing fraud models and verification workflows.
Our identity graph covers more than 5 billion identities and 740 billion signals. The world's largest payment networks, ecommerce marketplaces, and digital wallet platforms trust Pipl to get it right.
About the Role
As a Data Scientist, you will utilize machine learning, programming and domain knowledge to mark trustworthy transactions in an ever-changing domain. Your job will involve quantitative and qualitative analysis of real fraud. You will work closely with other teams, including Product, MLOps and Analytics, to review business problems and identify data requirements necessary to develop predictive models that can help solve them.
About the Candidate
The ideal candidate has a strong background in statistics, machine learning, research design, data analysis, writing production-level code, as well as excellent communication and organizational skills. English proficiency is a must. This position is for a “hands-on” type of person who enjoys tackling real-world data challenges with a can-do attitude and passion for data.
Responsibilities:
- Design and implement ML pipelines using structured and unstructured data for prediction and classification.
- Develop predictive ML models to assess risk, and support operational decision-making.
- Produce explainable model outputs, including key drivers and reason codes appropriate for non-technical users.
- Help define and track success metrics aligned to business value, not just model performance.
- Actively troubleshoot and refine customer models in production.
- Data Science professional with 2+ years of experience.
- Master’s degree in Statistics, Computer Science, Mathematics, or a related field.
- Candidates must have a demonstrated research experience.
- Strong proficiency in Python, SQL, and modern ML frameworks
- Proven record of applying machine learning algo to solve real world problems, including regression, classification, unsupervised learning.
- Skilled in handling, cleaning, analyzing, and presenting data.
Preferred
- Experience with working on identity data or in the fraud prevention industry
- Familiarity with cloud technologies (AWS/GCP/Azure).
Skills Required
- Data Science professional with 1-2 years of experience
- Master's degree in Statistics, Computer Science, Mathematics, or a related field
- Demonstrated research experience
- Strong proficiency in Python, SQL, and modern ML frameworks
- Proven record of applying machine learning algorithms to solve real world problems
- Skilled in handling, cleaning, analyzing, and presenting data
What We Do
Pipl is the identity trust company that makes sure no one pretends to be you. We do this by understanding the deep connections between the data elements that make up an identity and looking at the big picture. We analyze the relationships of many identifiers such as email, mobile-phone and social-media data that spans the globe. Our identity resolution engine continuously collects, cross references and connects identity records to create data clusters across the internet and numerous exclusive sources. The result is a searchable index of more than 3.5 billion identity profiles comprising over 3.6 billion phone numbers and 1.7 billion email addresses, with coverage in more than 150 countries. Our API and manual review solutions allow merchants to provide frictionless customer experiences and approve more transactions while reducing chargebacks and the risk of fraud.
