Senior investor and operator focused on fintech and growth-stage technology companies, with experience in product strategy, capital markets and corporate development. Dina Fliss is known for sourcing and scaling revenue-driven opportunities, serving on operating teams, and advising startups on go-to-market and fundraising. Active across venture and private equity ecosystems, she evaluates SaaS, payments and data-driven B2B models and partners with founders on commercialization, strategic partnerships and KPI-driven growth initiatives.
Senior investor and operator focused on fintech and growth-stage technology companies, with experience in product strategy, capital markets and corporate development. Dina Fliss is known for sourcing and scaling revenue-driven opportunities, serving on operating teams, and advising startups on go-to-market and fundraising. Active across venture and private equity ecosystems, she evaluates SaaS, payments and data-driven B2B models and partners with founders on commercialization, strategic partnerships and KPI-driven growth initiatives.
Seasoned operator-investor deploying growth capital into fintech and SaaS companies with revenue-driven playbooks. Focuses on commercialization, payments and data-enabled B2B models, favoring repeatable unit economics and clear KPIs. Blends strategic capital with operating support — go-to-market, partnerships and product-market fit acceleration — prefers growth-stage equity, concentrated portfolio stakes, multi-year horizon and active board-level oversight to de-risk scaling and optimize exit optionality.
Seasoned operator-investor deploying growth capital into fintech and SaaS companies with revenue-driven playbooks. Focuses on commercialization, payments and data-enabled B2B models, favoring repeatable unit economics and clear KPIs. Blends strategic capital with operating support — go-to-market, partnerships and product-market fit acceleration — prefers growth-stage equity, concentrated portfolio stakes, multi-year horizon and active board-level oversight to de-risk scaling and optimize exit optionality.
| Trades 754 | Longs Won 449/754 59% | Profit Factor 7.34 |
| Profitability | Shorts Won 0/0 0% | Standard Deviation $955,777.91 |
| Average Win $188,884.9 | Best Trade (Jul 10) $23.93M | Sharpe Ratio -9.68 |
| Average Loss -$37,866.47 | Worst Trade (Jul 17) -$3.87M | Z-Score -1.87 (96.02%) |
| Commissions $0 | Avg. Trade Length 7m 4w | Expectancy $97,161.86 |
| Loss Size | 100% | 90% | 80% | 70% | 60% | 50% | 40% | 30% | 20% | 10% |
| Probability of Loss | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | <0.01% | 0.01% |
| Consecutive Losing Trades | 11,364 | 10,227 | 9,091 | 7,955 | 6,818 | 5,682 | 4,545 | 3,409 | 2,273 | 1,136 |