1) Data Analysis:▪ Conducted A/B tests for the pre-IPO platform resulting in a 20% increase in CTR and a 10% increase in conversion rate ▪ Developed a Logistic regression to improve ROI, LTV, and CAC of the marketing strategy for the pre-IPO platform▪ Implemented a K-means clustering to classify investment targets generating a 30% time efficiency gain for the team▪ Deployed a Random Forest regression model to predict post-money valuation of late-stage SaaS startups in… Show more 1) Data Analysis:▪ Conducted A/B tests for the pre-IPO platform resulting in a 20% increase in CTR and a 10% increase in conversion rate ▪ Developed a Logistic regression to improve ROI, LTV, and CAC of the marketing strategy for the pre-IPO platform▪ Implemented a K-means clustering to classify investment targets generating a 30% time efficiency gain for the team▪ Deployed a Random Forest regression model to predict post-money valuation of late-stage SaaS startups in FinTech▪ Built and automated an ETL process for sourcing database incorporating data from 7 sources and 60+ parameters▪ Identified a high credit risk (PD) in the credit portfolio of an Indian BNPL service by utilizing statistical testing 2) Financial Analysis:▪ Conducting a valuation due diligence for a $20M deal in the FinTech sector▪ Analyzed 15 FinTech ventures using DCF modeling, sensitivity, and scenarios analysis resulting in a $3M deal▪ Proposed a scoring methodology for pre-IPO ventures combining qualitative scores and quantitative data3) Team Leadership & Client management:▪ Managing a cross-functional team of 3 analysts resulted in a 15% time efficiency gain per a venture analysis▪ Prepared 5 presentations for 2 startup CEOs and 1 investment banking VP resulting in a negotiation for a $10M deal Show less

▪ Deployed a Random Forest classification model that reduced the average credit portfolio PD by 12%▪ Improved a credit approval process, which increased the approval rate by 15 p.p. and reduced costs by 10%▪ Formulated a Go-to-Market strategy for a new credit product that attracted 150 new leads in the first month ▪ Proposed a partnership scoring matrix that allows an automated clustering based on 20+ metrics

▪ Developed a methodology using 30+ criteria to benchmark 15 railway companies and formulate a strategy for a client▪ Calculated IQ Index of 1,000 cities based on 30+ metrics using Python and pitched the results to a public client

•Performed Market and Industry Research to assess the feasibility of the business model, created Minimum Viable Product with a website and mobile app prototype.•Led a team of 5 people. Built and nurtured relationships with stakeholders and mentors resulted in a pitch opportunity in front of potential investors.

•Collected 20 best innovation practices for the Arctic Innovation Development Strategy resulted in a presentation with 3 private companies.