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Salary
$77k – $129k per year
Location
In office (Minneapolis)
Employment
Full-Time
Overview
Company
Impact
Profile match
RELX is a global provider of information-based analytics and decision tools headquartered in London, United Kingdom. Tracing its roots back to 1880 and formerly known as Reed Elsevier, the publicly traded conglomerate operates across four primary business segments: Risk, Scientific, Technical & Medical, Legal, and Exhibitions.

About the Business:

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com

About our Team:

The Analytic Customer Management team at LexisNexis Risk Solutions delivers actionable insights that fuel product growth. We respond to client needs, conduct both proactive and reactive analyses, and present findings that help uncover new opportunities. It’s a fast-paced, problem-solving environment where each day brings fresh challenges and meaningful impact.

About the Role:

We’re seeking a Data Scientist to lead predictive modeling and statistical analysis across domains such as risk and fraud. The ideal candidate will bring strong expertise in data mining and analytics to deliver impactful insights and scalable solutions.

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.

Requirements

  • 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.

Working for you

We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

  • Health Benefits: Comprehensive, multi-carrier program for medical, dental and vision benefits

  • Retirement Benefits: 401(k) with match and an Employee Share Purchase Plan

  • Wellbeing: Wellness platform with incentives, Headspace app subscription, Employee Assistance and Time-off Programs

  • Short-and-Long Term Disability, Life and Accidental Death Insurance, Critical Illness, and Hospital Indemnity

  • Family Benefits, including bonding and family care leaves, adoption and surrogacy benefits

  • Health Savings, Health Care, Dependent Care and Commuter Spending Accounts

  • In addition to annual Paid Time Off, we offer up to two days of paid leave each to participate in Employee Resource Groups and to volunteer with your charity of choice

Learn more about the LexisNexis Risk team and our culture here.

The salary range provided in this posting is the base salary range for Minnesota:$77,300.00 - $128,700.00

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-855-833-5120.

Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scamshere.

Please read our Candidate Privacy Policy.

We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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