Project Manager Technical: Customer Service Knowledge Domain Expert

  
 

About the job: 
At Red Hat the Global Customer Service team offers an inclusive and collaborative environment where professionals work together to build solutions for data quality, governance, and knowledge management. Red Hat is the place for you to apply your skills in data quality, AI, and project management while nurturing your leadership capabilities. The Global Customer Service team is looking for a Project Manager TechnicalCustomer Service Knowledge Domain Expert to join us in Pune, India. In this role, you will report to the Global lead of customer service and work on shaping the Red Hat Global Customer Service teams data quality and governance strategies, collaborating with AI experts, data scientists, engineering, and business stakeholders. Youll be responsible for defining standards, implementing quality programs, and driving continuous improvement across our customer data and knowledge systems. You will ensure our data and processes meet the highest standards of accuracy, security, searchability and usability, enabling effective self solve solutions and decision-making. As a Project Manager in this team, you will gain deep insights into AI applications, data governance practices, and enterprise-level customer service operations. In this role, you will have the opportunity to showcase your leadership skills, drive impactful solutions to complex challenges, and contribute meaningfully to Red Hats success while gaining broad visibility across the organization.  

What will you do? 

Contribute to defining, evolving and collaboratively executing the data quality and governance strategy for customer data, knowledge bases, and support records

Lead the implementation of data quality standards, metrics (KPIs), validation routines, and feedback loops across knowledge assets and customer interaction records

Collaborate closely with AI model developers, engineers, and business stakeholders to align data quality efforts with AI initiatives and product improvements

Guide process design for continuous data quality monitoring and implement automated validation tools and best practices

Champion a culture of data quality and governance, conducting training and communication programs to drive awareness and adoption

Evaluate and recommend data quality tools and technologies, including KCS V6 practices and AI-powered solutions

Develop domain-specific quality programs focused on Knowledge Management, support case quality, and voice of customer insights

Act as a prompt engineer for AI-assisted support tools, ensuring accuracy, efficiency, and security in AI-driven customer interactions

Monitor and report on data quality metrics, perform root cause analyses, and drive corrective and preventive actions across teams

  
 

What will you bring? 

Bachelors degree in Data Science, Computer Science, Information Systems, Business Analytics, or a related field

8-10 years of experience in data quality, governance, or data management, with at least 2 years in a leadership or project management role

Experience implementing enterprise-level data quality strategies, ideally supporting AI/ML initiatives

Familiarity with knowledge management systems (e.g., CMS platforms) and CRM tools like Salesforce Service Cloud

Strong understanding of data lifecycle management, profiling, cleansing, validation, and quality dimensions

Excellent communication and stakeholder management skills, with the ability to influence and align cross-functional teams

Solid project management skills and the ability to handle multiple initiatives in a fast-paced environment

Conceptual understanding of AI/ML and their reliance on high-quality data

Passion for continuous learning and driving data-driven improvements

  
 

The following are considered a plus: 

Certifications in Data Governance, Data Quality, or Project Management methodologies

KCS V6 Certification

Experience with natural language processing (NLP) applications and challenges in unstructured data quality

Familiarity with responsible AI practices and data ethics principles

Familiarity with industry best practices for data ethics and responsible AI

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