VC & startup solutions with an industrial engineering mindset
I design and implement data-driven venture capital workflows—from automated deal-flow scoring to comprehensive fundraising support—that improve real investment metrics. I combine hands-on VC analytics with B.Sc. studies in Industrial Engineering at TU Berlin to keep projects grounded in operations and ROI. Based in Berlin, I work fast, document clearly, and deliver measurable results.
Services
Data-driven venture capital and startup funding solutions
VC Deal-flow Scoring
Text + metadata ranking to prioritize inbound pitch decks using embeddings, gradient-boosting, and SQL. Research-grade analysis with transparent scoring features.
Benefits/uses: Faster triage, transparent scoring, data-driven investment decisions.
Pitch Deck Assessment
Comprehensive analysis of startup pitch decks including market sizing, business model validation, competitive positioning, and financial projections review.
Benefits/uses: Risk assessment, investment readiness scoring, founder feedback.
Startup Funding & Fundraising Support
End-to-end fundraising assistance including investor matching, due diligence preparation, valuation modeling, and pitch optimization for startups.
Benefits/uses: Higher success rates, better valuations, faster funding cycles.
Workflow Automation Implementation
Streamline VC operations with automated deal sourcing, portfolio monitoring, reporting pipelines, and investor communication workflows.
Benefits/uses: Operational efficiency, scalable processes, data-driven insights.
About
Bio
I'm a venture analyst and data scientist focused on turning investment data into actionable insights for VCs and startups. I study Industrial Engineering (Wirtschaftsingenieurwesen, B.Sc.) at TU Berlin, which keeps my approach practical—clear problem framing, lean processes, and measurable outcomes in the venture capital context.
Alongside university, I've specialized in VC operations through projects ranging from automated deal-flow scoring to comprehensive due diligence workflows. I'm comfortable across the investment stack: deal sourcing, evaluation, portfolio management, and fundraising automation.
In Berlin's venture ecosystem I've observed how successful funds validate investment theses quickly and scale when the metrics justify it. My tooling is modern and efficient (Python + ML libraries, financial modeling, CRM integrations), and I prefer transparent scoring systems and clear dashboards over black-box investment decisions.
My goal on every engagement is the same: deliver data-driven solutions that improve key investment metrics—deal flow quality, due diligence speed, portfolio performance tracking, or fundraising success rates.
Key Skills
Investment Analysis
deal-flow scoring, due diligence automation, market research, competitive analysis
Data Science & ML
embeddings, gradient-boosting, financial modeling, risk assessment
Venture Operations
portfolio monitoring, fundraising workflows, investor relations, KPI tracking
Technical Stack
Python, SQL, CRM integrations, financial APIs, dashboard development
Industrial Engineering
process optimization, workflow automation, ROI analysis (Berlin-based)
Portfolio
Venture capital and startup funding projects showcasing data-driven solutions
VC Deal-flow Scoring
Text + metadata ranking to prioritize inbound pitch decks using advanced ML techniques for investment decision support.
Tech Stack:
Embeddings, gradient-boosting, SQL
Outcome:
Faster triage; transparent scoring features
Startup Portfolio Analytics
Automated monitoring and reporting system for portfolio companies with KPI tracking, risk assessment, and performance benchmarking.
Tech Stack:
Python, SQL, Tableau, APIs
Outcome:
40% faster quarterly reviews; early warning system for at-risk investments
Fundraising Pipeline Automation
End-to-end workflow automation for startup fundraising including investor matching, outreach sequencing, and progress tracking.
Tech Stack:
CRM integration, Python, Email APIs, SQL
Outcome:
3x increase in investor response rates; 50% reduction in fundraising cycle time
Market Intelligence Dashboard
Real-time competitive landscape analysis and market trend identification for investment thesis development.
Tech Stack:
Web scraping, NLP, Time-series analysis, Visualization
Outcome:
Comprehensive market insights; data-driven investment decisions
Due Diligence Automation
Automated financial model validation, document analysis, and risk assessment for investment due diligence.
Tech Stack:
OCR, Financial modeling, Python, ML validation
Outcome:
60% faster due diligence; standardized risk scoring
Contact
Tell me what you need built or improved. I'll reply with a short plan, timeline, and a fixed or milestone-based quote.