Kenvue 目前正在招聘 a：
我们做什么
在 Kenvue，我们意识到日常护理的非凡力量。我们以一个多世纪的传统为基础，植根于科学，是标志性品牌的品牌 - 包括您已经熟悉和喜爱的 NEUTRGENA®、AVEENO、TYLENOL®®、LISTERINE®、JOHNSON'S® 和 BAND-AID®。科学是我们的热情所在;关心就是我们的才能。
我们是谁
我们的全球团队由 ~ 22,000 名才华横溢的员工组成，他们的职场文化中，每个声音都很重要，每一个贡献都受到赞赏。 我们热衷于洞察， 创新并致力于为我们的客户提供最好的产品。凭借专业知识和同理心，成为 Kenvuer 意味着每天有能力影响数百万人。我们以人为本，热切关怀，以科学赢得信任，以勇气解决——有绝佳的机会等着您！加入我们，塑造我们和您的未来。有关更多信息，请单击 here.
Role reports to：
SR MGR GLOBAL CV SYSTEMS & TRENDING位置：
Latin America, Brazil, Sao Paulo, Sao Paulo工作地点：
混合你会做什么
The Data Analyst plays a critical role in supporting the Global Complaint Vigilance (GCV) team by driving complex analytics initiatives and developing scalable solutions for global business partners. This role is embedded within a team that leads strategic efforts in Data Governance, Database Infrastructure, and Advanced Data Analytics for the Global Complaint platform.
Key Responsibilities:
Data Discovery & Ingestion: Support data analytics efforts by discovering relevant data within the Complaint Data Lake (CDL) and facilitating ingestion from source systems.
Stakeholder Collaboration: Partner with system owners to understand upstream processes and data inputs, ensuring alignment and data integrity.
Data Foundation Strategy: Lead initiatives to establish and maintain a robust data foundation in CDL, ensuring availability and curation of key quality data to support analytics and reporting needs.
Advanced Analytics Development: Design and implement innovative data science methodologies to support global metrics, trend analysis, and risk management planning.
Complaint Trend Analysis: Perform periodic analysis of complaint data to identify trends, risks, and opportunities for process improvement.
Cross-Functional Engagement: Collaborate with global stakeholders including Consumer Care Centers, Medical Safety, Business Quality, Marketing, Internal Affiliates, and External Sites to ensure consistency and compliance in the complaint vigilance process.
Training & Documentation: Assist in developing training materials and updating controlled documents related to complaint vigilance systems and processes.
Core Technical Skills & Tools:
Power BI Development – dashboard creation, data visualization, and report automation.
DAX (Data Analysis Expressions) and Power Query for advanced data modeling and transformation.
SQL for querying and managing relational databases.
ETL (Extract, Transform, Load) processes for data integration and pipeline development.
Data Lake Architecture and Data Governance principles.
Python or R for statistical analysis and predictive modeling (as applicable).
Familiarity with Azure Data Services or other cloud-based data platforms.
What we are looking for:
Requirements:
A minimum of a bachelor’s degree is required. A focus in Data Science/Engineering, or related field is preferred.
Experience in hands-on experience in complaint analysis or related roles
Proven experience in data analytics, preferably within a regulated or quality-focused environment.
Strong understanding of data lake architecture and data governance principles.
Proficiency in data querying, visualization, and statistical analysis tools.
Proficiency in Microsoft Power BI or equivalent data visualization tools
Excellent communication skills and ability to work across global teams and functions.
Other Preferred Experiences:
Experience supporting manufacturing, packaging, and development operations is preferred.
Experience supporting aspects of the complaint vigilance life cycle for pharmaceutical, device, and /or cosmetic products is preferred.
Experience with one or more technologies supporting complaint handling preferred.
Expertise with root cause analysis techniques including but not limited to: Brainstorming, data analysis and collection tools, 5 Whys, Fishbone (Cause and Effect), FMEA, and DMAIC preferred.
Experience with tools and techniques supporting qualitative risk analysis including but not limited to: Probability and impact assessment (likelihood of recurrence and potential effect), probability and impact matrix (risk ratings, rating rules), and risk categorization (by root causes, other qualifiers) is preferred.
Experience with systems and tools supporting analysis and reporting preferred.
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