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The Data Scientist will be joining the LexisNexis Risk Solutions Global Products and Analytics Team developing and supporting analytics products. Using Data Science principles, you will be collaborating with other teams to receive, analyze and manipulate data, along with creating processes and tools to assist customers in making decisions about their data.
• Develops data-driven testing methodologies using LexisNexis data assets.
• Clarifies and/or enhances stakeholder understanding of test objectives and the data required to support the test.
• Analyzes client data to prepare for analysis. Identifies data quality concerns and opportunities (i.e. performance indicators, scores, approval/declination indicators, etc.) and clearly communicates these concerns to stakeholders along with suggestions for preventing these issues closer to project initiation.
• Executes database queries to extract and aggregate LN’s data assets into attributes that are used for predictive modeling, reporting, and analysis.
• Creates and enhances the code and tools that support data processes. Tools are easily usable by a wide audience, including teams outside of Data Science.
• Creates and enhances GUI-based reporting tools that allow team members to gain insights about LexisNexis data assets.
• To review data results and communicate findings to stakeholders.
• Enforces data quality testing best practices.
• Bachelor’s degree in computer science, mathematics or statistics (or equivalent years of experience).
• Experience in data manipulation, cleansing and modeling (1-3 years) or equivalent demonstration of core skill set required to satisfy project requests.
• Experience in at least one general-purpose language (such as Python, R, Java) required or equivalent analytic software.
• Experience with SQL and/or other database technologies.
• Experience with using Git.
• Ability to quickly learn new technologies and programming languages.
• Excellent attention to detail, organization and documentation.
• Strong oral and written communication skills, including the ability to describe analytical results to non-statistical audiences.
• Analytical and problem-solving skills to help perform detailed data verification and validation.
• Experience with data manipulation and cleansing of large data sets and the ability to match and merge data elements.
• Basic knowledge of Excel and Word.
• Comfortable working in a fast-paced environment.