CLP

DIGITAL TECHNOLOGY
LEADERSHIP PROGRAM


The Digital Technology Leadership Program (DTLP) develops a pipeline of digital minded leaders for future end-to-end business needs in the Digital Technology function.
 
The program consists of four rotations each lasting a duration of six months. It is an immersive experience that will help you accelerate your leadership potential while enhancing your technical expertise. In addition to your rotational experience, you will receive education, personal mentorship, and a host of experiences from the top minds in your field while working on real world projects and initiatives.

  • Two year on-program experience comprised of performing meaningful customer-focused work

  • Four 6-month rotations resulting in real roles, impact, and responsibility

  • World-class education opportunities:

    • Industry, Product & Leadership Training

    • Ability to earn a Master’s Degree credits and other opportunities

    • ​​​​Train, work and learn with a globally diverse community of young professionals

Disaster Recovery & Business Crisis Impact Leader

In this group rotation three DTLPs were responsible for a big project, addressing the biggest cyber risk today: ransomware; by using a risk-based approach. Leading the initiative to help application owners of the 20+ most critical applications to create a resilient environment to build disaster recovery plans while ensuring these plans are aligned with the business expectations by creating business impact analysis. This enables application owners to test disaster recovery plans and report outcomes for audit purposes. Our activities included regular updates towards CIOs; working together with a network of 80+ people; process engineering; application architecture analysis etc.

Optimization & Machine Learning 

GE in-house tool APOW (Automated Process and Optimization Workbench) is used across all GE businesses. It is used by design teams to develop workflows which enable them to create standard work for their calculation process to ensure knowledge capturing and reduce/eliminate human errors. APOW links these workflows to mathematical functions to execute Design of Experiments (DoE), Optimization and Uncertainty Quantification (UQ). In this project we want to extend the mathematical modelling capabilities of APOW, by implementing the latest Optimization and ML methodologies being currently developed by our academic partners. As a DTLP we work on technical tasks, like reviewing and integrating Optimization methodologies, advance in ML applied to engineering applications and have the opportunity to practice, lead project management and planning in the team.

Hear from a DTLP member