In a recent discussion on GIAI Square, a student raised concerns about networking opportunities in the SIAI 2.0 AI MBA program, particularly about the strength of the alumni network and its impact on career opportunities post-graduation. As a professor and industry professional, I provided my perspective based on both academic experience and real-world industry exposure.

With the AI MBA program now divided into technical and business tracks, it is essential to address both career paths. This article focuses on networking for technical students—those who aim to work in AI/DS engineering roles or quantitative finance, where expertise matters more than connections alone.

Networking in STEM: Beyond Job Fairs and School Prestige

During my own education, job fairs were frequent, with Fortune 500 companies sending HR teams to present their hiring strategies. However, let me be blunt—these events were not about hiring top talent. They were primarily about company branding and industry presence. 99.9% of attendees did not walk away with job offers.

For technical professionals, the challenge is clear: your value must be demonstrated through skill, not just credentials. Schools provide a foundation, but true expertise comes from independent work. The best networking strategy for technical students involves:

• Building a strong GitHub presence: Employers often search candidates online. A well-documented portfolio of math-heavy AI/DS projects will do more for your career than a LinkedIn profile alone.
• Engaging in open-source AI projects: Actively contributing to repositories increases visibility among technical hiring managers.
• Participating in AI/DS research communities: Whether through Kaggle competitions, research publications, or AI forums, showcasing expertise is critical.
• Technical blog writing and discussions: Sharing insights on AI/DS applications and mathematical concepts demonstrates thought leadership.

SIAI’s Approach to Technical Networking

Unlike traditional MBA programs, where networking often means connecting with HR teams, SIAI’s technical track focuses on peer and mentor-driven networking. Our approach emphasizes:

• Direct engagement with senior AI researchers: Instead of prioritizing HR-led job fairs, SIAI hosts expert-led discussions on hiring preferences.
• Research-driven networking: SIAI students gain access to real-world AI/DS projects, allowing them to work alongside experienced professionals.
• GIAI’s Future Investment Arm: As part of its long-term vision, GIAI—the mother institution of SIAI—plans to launch its own investment vehicles, including a hedge fund focused on computational finance. This initiative will provide high-performing technical students with opportunities to transition into quantitative finance roles and AI-driven investment research.

What Really Matters for AI/DS Professionals?

For technical track students, networking is not about the quantity of connections but the quality of expertise. AI hiring decisions are often made by technical leaders, not HR representatives. Companies look for candidates who demonstrate:

• A deep mathematical and computational foundation
• Strong problem-solving abilities in AI and Data Science
• Independent research or engineering contributions

If you focus on becoming an expert in your domain, networking will follow naturally—senior researchers and industry professionals will recognize your work and recommend you for opportunities.

More information available in the discussion thread.