Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Backed by Correlation Ventures.
About the role
Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.
What they're looking for
- At least 3 years of experience in data science, machine learning, statistical modeling, or a related quantitative field
- Bachelor's or advanced degree in computer science, statistics, engineering, data science, or another quantitative discipline
- Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models
- Demonstrated experience creating meaningful fraud features
- Proficiency in Python and PySpark, with experience writing modular and tested code for large datasets and distributed or cloud data systems
- Experience using common data science and machine-learning tools such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies
More about this role
Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms.
We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model.
You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions.
We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization.
This is a remote role and you...
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