ABOUT PATSNAP

Patsnap empowers IP and R&D teams by providing better answers, so they can make
faster decisions with more confidence. Founded in 2007, Patsnap is the global leader
in AI-powered IP and R&D intelligence. Our domain-specific LLM, trained on our
extensive proprietary innovation data, coupled with Hiro, our AI assistant, delivers
actionable insights that increase productivity for IP tasks by 75% and reduce R&D
wastage by 25%. IP and R&D teams collaborate better with a user-friendly platform
across the entire innovation lifecycle. Over 15,000 companies trust Patsnap to
innovate faster with AI, including NASA, Tesla, PayPal, Sanofi, Dow Chemical, and
Wilson Sonsini.

QUALIFICATIONS

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• Educational Background: Bachelor’s degree or above in computer science, data engineering, software engineering, or related fields; at least 3 years of experience in data development.

• Technical Skills: Proficiency in Java or Python with a solid programming foundation; Familiarity with basic components of big data processing, such as Flink and Spark, with experience in performance optimization preferred; Familiarity with Linux systems and common operations.
• Ability Requirements: Strong sense of responsibility and proactive learning ability, able to adapt flexibly in a fast-paced environment; Clear thinking with excellent communication skills and team spirit, able to collaborate effectively with team members from diverse cultural backgrounds; Basic understanding of data privacy and compliance, capable of working in a regulatory environment.

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• Data Processing: Manage daily operations of patent data and related data, including integration, parsing, storage, and backup, ensuring data security, stability, and availability.

• Data Requirement Analysis and Implementation: Analyze and understand new data requirements, design data processing solutions, write relevant documentation, and implement code.
• Data Quality Management: Monitor data quality, design and implement targeted optimization solutions, and continuously improve data integrity and accuracy.
• Data Processing Framework Optimization: Optimize data processing frameworks and performance, enhancing the efficiency of system data handling to support rapid iteration of product data.
• Architecture Collaboration: Work closely with data architects, participate in architecture upgrades and updates, and enhance system scalability and stability.