Data Scientist
RELX

Job Info

---

LexisNexis Risk Solutions

We develop industry-leading compliance solutions to help customers fight financial crimes and stay abreast of anti-money laundering, anti-bribery and corruption, and countering financing of terrorism legislations. LexisNexis Risk Solutions cares about our world and we would love for you to join our team if you want to help solve meaningful problems too.

Data Scientist - Associate

Opportunity to join a vibrant, collaborative team as an entry level statistical modeler conducting statistical analysis and building predictive models for a variety of uses and markets, including credit, fraud, telecommunication, retail, government and business. You would be expected to have a firm understanding of data mining, statistical methods and multiple modeling techniques. As part of the team you would be tasked with finding innovative ways to produce solutions to serve our customers and continually expand your expertise.

Responsibilities:

• Assemble, merge and parse large amounts of data to detect meaningful trends and patterns
• Explore opportunities to enhance existing data with new features or evaluate new data sources to improve product solutions
• Develop analytic models, create and provide analysis code, and work with internal or external stakeholders to validate accuracy of production code.
• Consult with internal and external clients regarding analytic solution implementation, score calculations, analytic output, and statistical techniques
• Interpret, document and successfully communicate analytic work, requirements, and/or results to stakeholders, including those in non-analytic roles
• Conduct analysis and produce reports in support of both existing and new product development and/or customer sales
• Understand and execute analytic plans with the appropriate statistical or modeling technique
• Proactively identify and communicate data quality issues and successfully work with other teams to implement solutions
  

Required Qualifications:

• Bachelor's degree in statistics, data science, mathematics or quantitative methods (or equivalent years' experience)
• 1-2 years' experience building predictive models or conducting data analysis using R, SAS or similar software package.
• Knowledge of statistical analysis methodologies and in particular machine learning.
• Experience with R, Python or similar software packages
• Experience with data manipulation techniques
• High degree of creative, analytical and problem-solving skills
• Ability to learn quickly and communicate effectively
• Ability to work effectively both independently and collaboratively
• Willingness to adapt to new techniques and an innovative attitude towards finding solutions
• Fluency with presentation and document programs such as PowerPoint, Word, Excel
  
  

Preferred Qualifications:

• Experience with multi-node computing platforms like Hadoop
• Experience with version control through Git
• Master's degree in statistics, data science, mathematics or quantitative methods
• Proficient in using R Markdown, or Shiny.
• Experience developing tools to aid in data science endeavors
  

Our team members are dedicated individuals striving to provide our clients with innovative solutions to their very real problems. If you are looking to be a member of a team providing world-class analytics and data-driven solutions for our partners and customers, we welcome you to apply!

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. If a qualified individual with a disability or disabled veteran needs a reasonable accommodation to use or access our online system, that individual should please contact 1.877.734.1938 or accommodations@relx.com.

Please read our Candidate Privacy Policy

This job has expired.