Data Analyst I

Requirements

• Bachelor's degree holder
• Open to fresh graduates with relevant internship or practicum, experience related to data analytics, data science, or data engineering.
• 1-3 years of experience in data manipulation, data cleansing, and/or data modeling (or equivalent internship experience) demonstrating the core analytical skill set required to support project requests.
• Experience with at least one general purpose programming or analytics language (e.g., Python, Spark, or R).
• Working knowledge of SQL and database technologies.
• Experience using Git or other version control systems.
• Familiarity with cloud based tools and platforms (e.g., Microsoft Azure or equivalent).
• Proven ability to manipulate, cleanse, match, and merge large datasets.
• Strong analytical and problem solving skills, with attention to data verification and validation.
• Basic proficiency in Microsoft Excel and Word.
• Comfortable working in a fast‑paced, collaborative environment.
• Ability to quickly learn new tools, technologies, and programming languages.
• Strong attention to detail, organization, and documentation.
• Excellent written and verbal communication skills, with the ability to explain analytical results to non‑technical audiences.
• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives.
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Additional Information

Job Summary: As an Analyst, you will be collaborating with teams to receive, analyze and manipulate data, along with creating processes and tools to assist customers in making informed decisions about their data. You will learn about different solutions and use a variety of Cloud and PC based tools. The Data Analyst will be helping to support analytics products for both internal projects and external customers. We also work alongside our Analytic Consulting and Modeling partners to provide data and product insights. Accountabilities: Develops data-driven testing methodologies using data assets. Clarifies and/or enhancing 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. Designing, testing, and/or executing automated file processing and data reporting workflows. Executing database queries to extract and aggregate data assets into attributes and scores that are used for predictive modeling, reporting, and analysis. Creating and enhancing the code and tools that support data processes. Tools are easily usable by a wide audience, including teams outside of Data Science. Creating and enhancing Python or Power BI-based reporting tools that allow team members to gain insights about LexisNexis data assets. Enforcing data quality testing best practices. Performing all other duties as assigned.

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