Data Scientist: Risk, Fraud & Predictive Modeling

LexisNexis Risk Solutions FL Inc. Company; Minneapolis (MN); On-site; USD 77,000 - 129,000; Full time

Benefits offered by this job

Health benefits

401(k) with match

Employee Share Purchase Plan

Wellbeing program

Paid time off

Job summary

LexisNexis Risk Solutions is seeking a Data Scientist to lead predictive modeling and statistical analyses across risk and fraud domains. The role focuses on data mining, analytics, and delivering scalable insights to inform product development.

You will build ML models using SAS, R, Python, and SQL, develop cloud-based analytic solutions, and collaborate with stakeholders to ensure model accuracy. Strong communication across teams is essential.

Qualifications

• Degree in Computer Science, Statistics, Mathematics, or related field.
• Experience in predictive modeling.
• Proficiency in SAS, R, Python, and Big Data technologies.
• Strong communication skills and ability to work cross-functionally.
• Fraud analytics experience is a plus.

Responsibilities

• Build and deploy machine learning models using R, Python, SAS, and SQL.
• Develop cloud-based analytic solutions and integrate them into enterprise systems.
• Analyze large datasets to uncover insights and support product development.
• Collaborate with stakeholders to validate scoring code and ensure model accuracy.
• Document methodologies and follow best practices in statistical modeling.

Skills

Predictive modeling

SAS

R

Python

Big Data

Cross-functional collaboration

Fraud analytics

Education

Computer Science, Statistics, Mathematics, or related field

Tools

SAS

R

Python

SQL

Cloud platforms

Job description

LexisNexis Risk Solutions is seeking a Data Scientist to lead predictive modeling and statistical analyses across risk and fraud domains. The role focuses on data mining, analytics, and delivering scalable insights to inform product development.

You will build ML models using SAS, R, Python, and SQL, develop cloud-based analytic solutions, and collaborate with stakeholders to ensure model accuracy. Strong communication across teams is essential.