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LexisNexis® Risk Solutions provides customers with solutions and decision tools that combine public and industry specific content with advanced technology and analytics to assist them in evaluating and predicting risk and enhancing operational efficiency. We use the power of data and advanced analytics to help our customers make better, timelier decisions. By bringing clarity to information, we ultimately help make communities safer, insurance rates more accurate, commerce more transparent, business decisions easier and processes more efficient. You can learn more about LexisNexis Risk at the link below: LexisNexis Risk Solutions

Data Scientist Intern

Full TimeRemoteTeam 1,001-5,000

Location

United States

Posted

7 days ago

Salary

Not specified

No structured requirement data.

Job Description

This internship is part of the Rising TIDE program. Elsevier created its Rising TIDE internship program to foster inclusion and provide individuals with work experience in the corporate world so that we can develop a pipeline of talent for entry-level individuals for post-graduation opportunities. We are looking for a Data Scientist Intern to join our team, working closely with the Legal department and our AI Data Science team to help identify potential copyright violations in third-party applications and AI models. You will support our content protection efforts by: Analyzing AI systems and applications Testing detection models Collaborating across teams to detect unauthorized use of Elsevier's copyrighted materials This is a unique opportunity to gain practical experience at the intersection of technology and law, working alongside some of the most advanced data scientists in the field. You will be heavily involved in projects that protect intellectual property and ensure regulatory compliance, making a meaningful impact on our business and the broader publishing industry. Location: This is a remote or home-based role, open to various locations in the Eastern and Central time zones. Program Dates: June 1, 2026 - August 7, 2026 Eligibility: Undergraduate or graduate students expected to graduate by May 2027. How to Apply: Candidates must submit a resume and demonstrate technical work by including one of the following: a link to a portfolio website or GitHub profile, code samples, or relevant academic or personal projects to be included on their resume or cover letter.

Job Requirements

  • Currently pursuing a bachelor’s or master’s degree in computer science, data science, statistics, mathematics, or a related field of study with a graduation date of May 2027
  • Demonstrate proficiency in Python and be familiar with libraries for machine learning, NLP, web scraping, or data analysis (e.g., scikit-learn, TensorFlow, PyTorch, HuggingFace Transformers, BeautifulSoup, Scrapy, pandas)
  • Experience with data analysis tools and techniques
  • Interest in legal and compliance topics related to AI and intellectual property (prior experience is a plus, but not required)
  • Able to work collaboratively and communicate effectively within a multidisciplinary team
  • Experience with testing, web scraping tools, APIs, Agile/Scrum, or version control (like GitHub) is advantageous, but not required
  • Solid analytical skills and a structured approach to problem-solving
  • Eager to learn and contribute in a supportive environment
  • Collaborate with Legal and AI Data Science teams to address challenges in content protection and compliance
  • Analyze third-party AI models and applications for potential unauthorized use of Elsevier content
  • Support the development and evaluation of AI models for detecting copyright infringements and compliance risks
  • Assist the legal team in preparing technical documentation for compliance cases
  • Analyze datasets, identify patterns, and communicate findings clearly to stakeholders
  • Document issues and suggest improvements to systems and processes
  • Learn best practices and advanced methodologies from experienced team members

Benefits

  • $28/hour for undergraduate students
  • $30/hour for graduate students

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