Data Scientist - Predictive Analytics for Risk & Fraud

RELX; Minnesota; On-site; USD 78,800 - 131,300; Full time

Benefits offered by this job

Health benefits

401(k) with match

Wellbeing program

Job summary

LexisNexis Risk Solutions is seeking a Data Scientist to lead predictive modeling and statistical analysis across risk and fraud domains. The role emphasizes data mining and analytics to deliver insights and scalable solutions.

The team operates in a fast-paced environment, collaborating with stakeholders to validate scoring code and ensure model accuracy, while documenting methodologies and best practices.

Qualifications

• Bachelor's or higher in Computer Science, Statistics, Mathematics, or related field.
• Experience in predictive modeling and data analytics.
• Proficiency in SAS, R, Python; familiarity with Big Data tech.
• Excellent communication and cross-functional collaboration skills.

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

Data mining

Statistical analysis

Strong communication skills

Cross-functional collaboration

Education

Computer Science

Statistics

Mathematics

Tools

SAS

R

Python

Big Data technologies

Job description

LexisNexis Risk Solutions is seeking a Data Scientist to lead predictive modeling and statistical analysis across risk and fraud domains. The role emphasizes data mining and analytics to deliver insights and scalable solutions.

The team operates in a fast-paced environment, collaborating with stakeholders to validate scoring code and ensure model accuracy, while documenting methodologies and best practices.