Kawehi W.

Experience

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  Merit Interactive Network Technology | Data Intelligence Service Provider

  Jun 2021 - Aug 2021

  Data Analyst

  Enhanced business performance and user satisfaction through data-driven analysis and optimization strategies.• Let the design of core metrics (daily user visits, conversion rates, average order value) to monitor business performance, enhancing client's ability to track operations and quickly identify data variations.• Conducted comparative analysis to identify dissatisfied users, providing actionable insights that helped refine interface improvements and personalized user journeys, increasing overall user satisfaction.• Implemented multi-armed bandit algorithms to optimize the onboarding process and introduced first-time user discounts, leading to a 19% increase in new user acquisition• Detected a sudden drop in user conversion rates and proposed optimization solutions using SQL and PowerBI. Identified slow-loading promotional pages affecting user experience, leading to restored revenue. Show less

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  TE Connectivity

  Feb 2022 - May 2022

  Project Lead

  Developed and implemented strategic M&A evaluation frameworks to support acquisition decisions and enhance financial performance.• Led the valuation of TE Connectivity's acquisition of Yamaichi Electronics, estimating $322 million in synergies, maximum purchase price of $939 million, and a 40% premium over the current stock price • Developed comprehensive risk management strategies and successfully mitigated risks in a $412 million financing plan by recommending a 50% debt and 50% cash approach • Analyzed and projected annual cost synergies, predicting cost savings of $50 million annually through optimized operational efficiency and elimination of redundancies • Formulated detailed integration plans, achieving a 20% increase in R&D productivity and a 15% reduction in transportation and distribution costs, enhancing overall operational performance Show less

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  MILESTONE ASSET MANAGEMENT

  Jul 2022 - Dec 2022

  Data Analyst

  Optimized customer engagement and business growth strategies using advanced analytics and personalized marketing.• Implemented K-means clustering to segment customers, providing insights and leading to an increase in engagement and conversion rates among high net-worth customers and active users.• Analyzed customer segments and developed personalized marketing strategies, resulting in increased investment returns and client retention, driving overall business growth by 10%.• Le the utilization of regression analysis and A/B testing to optimize advertising campaigns, promotional offers, and customer outreach strategies, leading to a 25% improvement in conversion rates and overall marketing ROI.• Analyze market trends to create bi-weekly reports, helping stakeholders in identifying opportunities and shaping future marketing strategies for the young investor market. Show less

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  Mondavi Center

  Sept 2023 - Jun 2024

  Data Analyst | Project Manager

  • Analyzed new season data using Python and Tableau to create a detailed dashboard, revealing a 53% revenue increase from the previous year and aiding stakeholders in understanding marketing strategy impacts.• Streamlined and enhanced post-show survey processes by implementing a quantitative analysis framework in Qualtrics and Python, improving engagement and satisfaction for an audience of 20K+, and achieving a 10% increase in repeat purchases.• Leveraged Google Analytics 4 to develop insightful reports and dashboards, executed A/B testing to refine digital advertising strategies, enhancing campaign efficiency by 30% and increasing profits by 15% in the first quarter.• Held weekly meetings with the Assistant Director of Business Intelligence and Revenue Management leadership to brainstorm and scope new analytic research ideas, presenting and explaining data-driven insights and recommended actions. Show less

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  Credit Risk Model Stability Kaggle Competition | Python, Machine Learning

  Apr 2024 - May 2024

  Data Scientist | Kaggle Competition

  • Currently utilizing advanced machine learning algorithms, including XGBoost, Random Forest, and Neural Networks, to build and refine predictive models for financial risk assessment, aiming to significantly enhance default risk prediction accuracy by 78%.