Job Responsibilities:

1. Be responsible for PC service parts data analysis: build up prediction and early warning models: Utilize historical Service data to achieve dynamic prediction of service parts abnormal, detect potential risks in advance, issue timely warnings, provide strong support for service quality control.

(1) In - depth AI - driven Data Mining of Spare Part Data: Apply advanced AI technologies, such as machine learning algorithms to comprehensively mine the PC service parts data. Precisely identify potential abnormal risk factors from massive service data.

(2) Data Visualization and Report Generation: Develop interactive data visualization interfaces to display key indicator, trend changes, and abnormal situations. Regularly generate high – service parts data analysis reports.

(3) Author executive summaries synthesizing quarterly impact assessments—quantifying cost savings realized through reduced truck rolls or improved first-contact resolution rates—and present these at leadership review boards.

2. Based on AI data analysis results, Be responsible for Cross-Border Project Management, Translate quantitative findings into tactical recommendations：

(1) Work closely with GEO service team, and other relevant units to jointly drive data-driven business process optimization and service innovation. Actively participate in cross-departmental project meetings and discussions, presenting insights and recommendations from an analytical perspective.

(2) Formulate detailed project implementation plans and milestone schedules based on project objectives and strategic directions, clearly defining key tasks and deliverables at each phase。

(3) Author executive summaries synthesizing impact assessments—quantifying cost savings realized through reduced truck rolls or improved first-contact resolution rates—and present these at leadership review boards.

Job Requirements:

1. Educational Background: A bachelor's degree or above, with a preference for majors related to computer science, data science,

2. Work Experience: 3 - 6 years of relevant data analysis experience, with at least AI data projects 2 years focused on data analysis of similar manufacturing products,

3. Familiar with entire supply chain process of PC parts production, inspection, storage, and transportation.

4. Skill Requirements:

(1) Be proficient in Python, master machine learning and deep - learning frameworks (such as TensorFlow and PyTorch), and be able to independently complete complex quality data mining and modeling tasks.

(2) Proficient in at least one mainstream data mining tool (such as RapidMiner, WEKA, etc.), with certain practical experience in feature engineering.

(3) Well-versed in deep learning frameworks (such as TensorFlow, PyTorch, etc.), possessing the ability to build and train deep learning models; those with relevant project experience in natural language processing are preferred

(4) Be good at using data visualization tools to create high - quality, highly interactive quality data reports and dashboards.

(5) Familiar with the use and management of relational databases (such as MySQL, Oracle, etc.) and non-relational databases (such as MongoDB, Redis, etc.), with the ability to write complex SQL query statements.

(6) Have basic English communication skills, be able to read English technical documents, and participate in international business exchanges.

Competency Requirements:

1. Have a keen sense of data and a strong understanding of business, be able to identify problems from quality data, analyze the causes, and propose solutions.

2. Have a deep understanding of computer spare parts engineering and the service supply chain business, be able to accurately grasp quality - related business pain points, and propose innovative solutions.

3. Have excellent communication and collaboration skills, be able to work effectively with people from different departments, and promote the implementation of quality improvement projects.

4. Have strong learning ability and an innovative spirit, be able to quickly master new technologies and knowledge, and continuously explore new data analysis methods and business application scenarios.

5. Have strong problem - solving ability and stress resistance, be able to independently solve technical and business problems in a complex business environment.