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In my role leading the launch of that new customer onboarding system, the pressure was really on. We had that tight three-month window and a lean team to build something that would be the first impression for all our new users. One specific hurdle we faced was integrating our legacy customer database with this brand-new system. The data structures were quite different, and initially, the data migration process was proving to be much slower and more error-prone than anticipated. This threatened to push our launch date.

To tackle this head-on, I didn't just delegate. I dove into the technical details with the lead engineer. We mapped out the data fields meticulously, identified the key transformation rules needed, and actually prototyped a more efficient data migration script using Python. This allowed us to automate a significant portion of the process that was previously manual.

Furthermore, to ensure we were building the right thing quickly, we implemented very short feedback loops. We'd build a small piece of the onboarding flow, get immediate feedback from a small group of internal users, and iterate based on their input. For example, early on, users found a particular step in the registration process confusing. Based on their feedback, we completely redesigned that screen within a couple of days, leading to a much smoother experience.

By getting into the technical weeds to optimize the data migration and by relentlessly focusing on user feedback through rapid iterations, we not only overcame the risk of a delayed launch but also delivered an onboarding experience that was significantly more user-friendly than initially envisioned. It was incredibly rewarding to see new customers move through the system so smoothly right from day one. That success really underscored the power of combining technical problem-solving with a user-centric approach

PCA, or principal component analysis, is a process to determine what are the most important features. I like to think about it in terms of eigenvalues. If you have a diagonal matrix, then the eigenvalues are the diagonal terms, and the eigenvectors are the basis vectors. In PCA, the principal components and their effects aren't as visible, but it is a process to determine them. If I recall correctly, PCA will return the absolute value of the eigenvalues, rather than the eigenvalue itself. 

My system will have the following components

a) Credit Check Service -> we use this to check the credit worthiness of the user 

b) Collect Credit card infomraiton - which will then be authorized to be charged in installments at a later time

c) A scheduler which schedules a payment to be charged 

• Self-Motivated: I thrive on challenges and take initiative to learn and grow without external prompts.

• Passion for Exploration: I love exploring new places, immersing myself in diverse environments, and gaining fresh perspectives.

• People-Centric: I enjoy engaging with new people, building connections, and exchanging ideas.

• Mentorship: Mentoring others is something I genuinely enjoy, as it allows me to share knowledge and help others grow.

• Appreciation for Art and Culture: I have a deep love for art, culture, music, and world cinema, which enriches my creativity and worldview.

• Cooking Enthusiast: I love experimenting in the kitchen and trying out new dishes, blending culinary skills with creativity.

During my career I manage a large number of projects, but one of the most challenging ones was the development and launch of a Telematic Control Unit for an important car maker in Japan. The project is important to me due to technical challenges we faced, the skills I applied and the impact on the company.

As Program manager my role was to lead a cross-functional team including Design Engineers, SW Engineers, Operations teams, Material procurement teams, quality and logistics, among others to successfully launch the product to mass production.

First, I kicked-off the project. For that, I shared with the team the project scope, the goals to achieve, I shared the project plan with key milestones and defined the roles and responsibilities for the different team members.

During execution, to maintain a clear communication, I set up design review sessions with the entire engineering organization to ensure everyone was aligned and fixing potential issues in advance. Besides, I led a weekly core-team meeting where we assigned tasks to team members, and setting clear deadlines for each task. To make these tasks easier to manage, I made sure tasks were broken down into manageable sizes with clear descriptions and acceptance criteria. This helped the team to stay focus on their deliveries and maintain speed.

Another important part was to manages Risks. I worked with the team to identify and classify possible risk that might impact to the project deliverables. We defined a Risk matrix to monitor during the development process, and also a project board with the main KPIs to achieve during each stage of the project.

To ensure quality, we defined different testing processes focused on the design, and the process. We installed specific quality check for the documents and devices delivered for each of the milestones of the PDP, as well as a clear quality control in mass production.

Finally, for transparency, I reported on monthly basis to our company leaders and customer about the project status, focusing on Timing, cost, quality and scope.

By employing these strategies, we successfully launched our module on time matching with all the requirements defined by our customer. This was a great example of teamworking, and taught me the importance of breaking down large projects into smaller and easy to manage pieces. Transparent and clear communication was also a great lesson learned. This approach helped me to improve in leading teams through complex problems.