We believe many of the most promising companies are not fundraising . They’re profitable, focused, and flying under the radar.

We’re looking for a Master’s student to explore how successful startups actually scale — and how early signals of that success can be identified before companies show up in VC deal flow.

The thesis will focus on:

• Analyzing successful startups & scale-ups
• Extracting patterns, KPIs, and early growth signals
• Building a data-driven model to identify scalable, bootstrapped companies

Paid thesis | Real VC use case
Munich preferred, remote possible
Start flexible (2026)

If you’re curious about venture capital, data, and what truly makes companies succeed — let’s talk.

DM me or reach out via EMAIL

2️⃣ University Career Portal Version (clear, formal, concise)

Master Thesis (m/f/d)

Data-Driven Identification of Scalable Startups | Venture Capital

Location: Munich (preferred) or remote
Start: Flexible (2026)

Description:
We are a venture capital fund focused on backing scalable B2B technology companies. For a Master’s thesis, we are seeking a student to research how successful startups scale and how early indicators of scalable success can be identified in bootstrapped companies that are not actively fundraising.

The thesis will involve analyzing growth patterns, structures, and KPIs of successful startups and developing a data-driven or data-supported framework to identify similar companies at an early stage.

Your profile:

• Master’s student in Data Science, Business Analytics, Entrepreneurship, Computer Science, Economics, or a related field
• Strong analytical skills and interest in startups and venture capital
• Ability to work independently in a research-driven, practical environment

What we offer:

• Paid Master’s thesis with high practical relevance
• Close collaboration with an operator-led venture capital team
• Flexible working setup and real-world application of your research

Application: