Senior Data Scientist, Fraud Risk Detection

Experian Information Solutions, Inc.; United States; Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Great compensation package and bonus

Medical, dental, vision, and 401K with

Remote, hybrid, or in-office

Flexible time off including holidays

12 paid holidays

Job summary

Experian Information Solutions, Inc. is seeking a remote-first senior data scientist to build ML models for fraud detection across account opening, takeover, and identity risk.

You’ll collaborate with researchers and engineers, turning insights into production-ready tools while upholding governance and privacy standards. The role emphasizes model governance, explainability, and responsible analytics within a fraud analytics context, with a focus on cross-functional collaboration and scalable

Qualifications

• 3+ years of experience in data science or related quantitative field.
• Bachelor’s or advanced degree in a quantitative discipline.
• Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection.
• Proficiency in Python, with the ability to write clean, readable, and well-tested code.
• Experience with Pandas, NumPy, and Scikit-Learn.
• Familiarity with PySpark and large-scale data tools and cloud platforms such as AWS, Google Cloud, Azure, Databricks, Snowflake, or similar.
• Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry via coursework, internship, or prior work.

Responsibilities

• Explore complex datasets to identify fraud patterns, attack methods, and signals.
• Collaborate with senior data scientists to translate fraud questions into testable hypotheses.
• Build ML models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using metrics like precision, recall, fraud capture rate, false-positive rate, and customer friction.
• Develop and validate features using identity, transactional, behavioral, and other data sources.
• Write clean, tested code and partner with engineering to productionize models and features.
• Work with the score monitoring team to set up model and feature monitoring and support related research.
• Prepare analyses and communicate findings to technical and nontechnical audiences.
• Apply data privacy, model documentation, explainability, validation, and governance standards.

Skills

Python programming

Data science

Machine learning

Statistical modeling

Feature engineering

Education

Bachelor's or advanced degree

Tools

Pandas

NumPy

Scikit-learn

PySpark

AWS

Google Cloud

Azure

Databricks

Snowflake

Job description

Remote-first senior role in fraud risk detection with Experian Information Solutions, focused on building machine learning models and features that identify high-risk activity while minimizing friction for legitimate customers. You will work across research and engineering to take findings and turn them into production-ready tools, supported by a team that emphasizes model governance and responsible analytics.

What you’ll do

• Explore complex datasets, with guidance from senior team members, to identify fraud patterns, attack methods, and behavioral signals.
• Collaborate with senior data scientists to translate fraud questions into testable hypotheses.
• Build machine learning models for fraud detection across account opening, account takeover, and identity risk.
• Evaluate models using both technical and business metrics, including precision, recall, fraud capture rate, false-positive rate, and customer friction.
• Develop and validate features using identity, transactional, behavioral, and other available data sources.
• Write clean, well-tested code and partner with engineering to bring models and features into production.
• Work with the score monitoring team to set up model and feature monitoring, and support research on related client questions.
• Prepare analyses and communicate findings to both technical and nontechnical audiences.
• Apply Experian standards for data privacy, model documentation, explainability, validation, and governance.

Requirements

• 3+ years of experience in data science, machine learning, statistical modeling, or a related quantitative field.
• Bachelor’s or advanced degree in a quantitative discipline (computer science, statistics, mathematics, economics, engineering, data science, or similar).
• Foundation in supervised learning, model evaluation, feature selection, statistical inference, and techniques such as classification and anomaly detection.
• Proficiency in Python, with the ability to write clean, readable, and well-tested code.
• Experience with tools such as pandas, NumPy, and scikit-learn.
• An investigative mindset to move from unusual data patterns to testable hypotheses.
• Familiarity with PySpark and large-scale data tools and cloud platforms such as Amazon Web Services, Google Cloud, Azure, Databricks, Snowflake, or similar.
• Exposure to financial services, FinTech, payments, or another regulated or fraud-intensive industry via coursework, internship, or prior work.

Tech stack

Python, pandas, NumPy, scikit-learn, PySpark, Amazon Web Services, Google Cloud, Azure, Databricks, Snowflake

Benefits

• Great compensation package and bonus plan
• Core benefits including medical, dental, vision, and matching 401K
• Flexible work environment: remote, hybrid, or in-office
• Flexible time off including volunteer time off, vacation, sick, and 12 paid holidays
• Explore additional benefits: https://yourexperianbenefits.com/cand-index.html

Role context: Experian’s Fraud Analytics & Commercialization includes client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. This is a remote role reporting into the Sr. Manager of Fraud Analytics.

Location: United States (remote) | #LI-Remote