Why I Left Finance
After five years as a Financial Analyst at Otto Group, I had built a solid career in corporate finance — budgeting, reporting, forecasting. But something kept pulling me toward data. Every spreadsheet I built, every report I automated, I found myself thinking: there has to be a smarter way to do this.
The turning point came when I started automating repetitive reporting tasks with Python scripts. What used to take days suddenly took minutes. That feeling of leverage — of building something that works for you — was addictive.
The Transition Plan
I didn't quit overnight. Here's what my transition looked like:
- Self-study phase (6 months) — Python, statistics, SQL on evenings and weekends
- Bootcamps (2022–2023) — Intensive programs at neuefische and Le Wagon covering ML, deep learning, and data engineering
- Master's degree (2023–2026) — M.Sc. Data Science & AI at FH Wedel for deep theoretical foundations
What Finance Taught Me About Data Science
The business background turned out to be my biggest advantage, not a liability:
- Domain knowledge matters — Understanding KPIs, unit economics, and business processes made me a better data scientist. I don't just build models; I build models that solve business problems.
- Stakeholder communication — Years of presenting to executives taught me to explain complex technical concepts in simple terms.
- Structured thinking — Financial analysis is fundamentally about finding patterns in data. Sound familiar?
The Hard Parts
Let me be honest about what was difficult:
- Imposter syndrome — Surrounded by CS graduates who'd been coding since they were 14. I started Python at 28.
- Math catch-up — Linear algebra, calculus, and probability theory needed serious refreshing.
- Income drop — Going from a full-time salary to student life was a real adjustment.
Where I Am Now
Today I work as a Data Scientist at Datalogue GmbH, building multi-agent AI systems that have reduced manual workload by 53%. I'm finishing my Master's thesis on multi-agent systems, and I've built projects spanning computer vision, NLP, recommendation systems, and full-stack applications.
The transition took three years of intense work. Was it worth it? Absolutely. I went from analyzing data in spreadsheets to building AI systems that generate insights autonomously.
Advice for Career Changers
If you're considering a similar move:
- Your previous experience is an asset — Don't minimize it. Domain expertise is rare and valuable.
- Start building immediately — Don't wait until you "know enough." Build projects from day one.
- Network in both worlds — Your finance network + your new tech network = unique opportunities.
- Be patient with yourself — A career transition is a marathon, not a sprint. Give yourself at least 2–3 years.