Naveen Raja Raja

Led and successfully executed three key projects in support of journal operations:

• Query Automation Project: Automated status queries and deadline extension queries using Machine Learning (ML) and Robotic process automation (RPA) capabilities, reducing manual workload and streamlining processes.
• Lean and Green Project: Implemented sustainability initiatives, reducing print copies and supporting environmental goals.
• Direct to Editor Assignment Project: Enhanced editor assignments, improving workflow efficiency and publication timelines using RPA capabilities.

Successfully delivered a critical phase of the query automation project, by integrating applications using ML and RPA which will help in reducing query costs by 25%.
Efficiently led the Lean and Green project, driving sustainability initiatives and contributing to our goal of achieving net-zero CO2 emissions by 2040.
Achieved a substantial cost savings of $2.6 million and a carbon emissions reduction of 850 metric tons in 2023, exceeding the target by $300 K. These results played a pivotal role in enabling informed decision-making and communicating progress to stakeholders at the steering committee.
Held a key responsibility for ensuring the timely delivery of use cases, and consistently meeting project deadlines.
Effectively communicate complex technical concepts to non-technical staff, fostering better cross-functional collaboration and understanding.
Collaborated closely with stakeholders to provide regular updates on project progress, ensuring alignment with project goals and objectives.

Played a pivotal role as a core member of the Celonis project team, leading initiatives that improved workflow efficiency.

Successfully configured, tested, and deployed new alerts to identify papers stuck in the publication process, resulting in a reduction in publication delays.

Integrated critical KPIs into a Celonis reporting dashboard, providing a comprehensive end-to-end view of the publication process and enabling data-driven decision-making.

Maintained the highest data quality standards by rigorously testing new features and functionality before release, resulting in a high data accuracy rate.

Managed the end-to-end process of 12 journals within the Computer Science portfolio, ensuring timely publication and improving submission-to-publication times.

Contributed as a project team member and played a key role in successfully completing the 'Reduction of Re-planning' project.

Developed customized macros that significantly improved the efficiency of journal managers, reducing manual tasks and enhancing overall workflow.

Designed and implemented the 'No Dues' Application, which streamlined author proof reminders.

Contributed to automating the renewal process for the GCS team, reducing manual effort and ensuring timely renewals for clients.

Served as a pilot member and later champion for OBIEE, Tableau, and Cupenya in RCO, leading successful data analytics projects and enabling data-driven decision-making.

Developed strategic roadmaps outlining key milestones and action plans, resulting in a consistent SLA achievement rate.

Monitored and analyzed productivity metrics, providing critical data for managerial reports, which led to process enhancements and an increase in team efficiency.

Effectively communicated system outage updates to stakeholders, reducing downtime through improved outage response protocols.

Forecasted SLA performance, proactively alerted stakeholders, and initiated control measures, ensuring SLA compliance during critical periods.

Regularly updated stakeholders with comprehensive hourly, daily, weekly, and monthly stats on advisors, teams, and processes, fostering transparency and informed decision-making.

Developed and implemented sequential troubleshooting procedures.

Mentored frontline executives, fostering skill development and increasing team efficiency.

Effectively monitored real-time queues using advanced tracking systems, optimizing staff allocation and reducing response times.

Generated and delivered detailed process statistics reports on an hourly, daily, weekly, and monthly basis, aiding in data-driven decision-making and process improvements.