Knowledge Management Data Analyst Intern

Lenovo•Morrisville, NC

1d•Onsite

About The Position

Lenovo is seeking a Knowledge Management Data Analyst Intern to join their team. This role focuses on improving the customer self-support experience by analyzing user behavior, optimizing knowledge models, and enhancing data platforms. The ideal candidate is passionate about technology and skilled in using data to solve problems.

Requirements

Currently pursuing a degree in computer science, statistics, mathematics, data science, or related fields.
Technical data analysis skills and mining capabilities, able to independently perform data cleaning, feature engineering, and model training.
English speaking skills are necessary for communicating with a global team.
Proficient in Python, SQL, and other data analysis languages and tools.
Proficiency in SQL for data querying
Experience with Excel and data visualization tools (e.g., Tableau, Power BI)
Strong problem-solving skills and ability to interpret data to drive decisions.
Ability to present findings clearly to both technical and non-technical stakeholders.
Ability to work independently and manage multiple projects with tight deadlines.

Nice To Haves

Experience with AI products related tools is a plus.
Experience in KM, knowledge graph, and natural language processing.
Proficient in Adobe Analytics.
Knowledge of regression, clustering, and predictive modeling.
Understanding of data privacy, security, and compliance standards.

Responsibilities

Analyze customer browsing data from Adobe Analytics to identify trends and improvement opportunities.
Evaluate internal knowledge usage using Power BI to uncover gaps and optimize content.
Support issue diagnosis by analyzing user behavior across multiple support channels.
Build and maintain data analytics platforms and dashboards.
Design metric systems and develop visualization tools to support decision-making.
Use AI tools to identify missing or underperforming knowledge content.
Track emerging search trends and compare them with historical data.
Analyze diagnostic results to identify potential product issues and support product improvements.
Collaborate with other analysts to align on best practices and share insights.
Explore new machine learning algorithms, LLM tools, and model architectures to enhance diagnostic performance.
Work with engineers to ensure accurate data collection, storage, and processing.
Partner with IT vendors to improve data quality and expand data fields.