I served as the lead QA engineer and acted as a liaison between Nisum and clients during the PDM review, ensuring timely deployment and facilitating seamless collaboration. I spearheaded the AI/ML product proof of concept (POC) initiative for smart décor recommendations, leveraging object detection and image classification algorithms to identify new opportunities for retail clients.Highlight of my achievements:• Led the QA engineering team in a successful data migration from Teradata to Snowflake, accelerating the project timeline by three months.• Boosted productivity by 15% for a major retail client at Nisum Technologies by designing and launching new analytical reports on business KPIs using MicroStrategy and SQL.• Developed an award-winning fashion trend prediction model leveraging data analytics and machine learning on large-scale image data in Nisum’s global hackathon.

I utilized smart battery data to investigate and predict battery failures for a leading agricultural technology company focused on equipping farmers with drones, robots, AI, and IoT solutions. I collaborated with cross-functional teams to improve the reliability and performance of advanced agricultural technologies.Highlights of my achievements: • Enhanced reliability of 50,000+ drones and reduced malfunctions by 15% through the formulation of time series models based on smart battery data analysis.• Contributed to the development of next-generation drones, robots, and autopilot features for agricultural machinery by collaborating with product managers and presenting key findings.

I created a royalty payment fraud detection model and managed performance dashboards for Xperi, previously known as DTS, which holds the world’s largest audio IP portfolio used by major cell phone brands. I improved business insights and decision-making by developing a revenue forecasting model.Highlights of my achievements:• Increased auditing accuracy and reduced audit time and costs by 50% at Xperi Corporation through the implementation of a royalty payment fraud prediction model using machine learning in R.• Delivered a 10% boost in operational efficiency by building and maintaining performance dashboards, conducting in-depth analyses, and offering actionable insights.• Improved revenue forecasting accuracy by 20% by constructing a model that leveraged financial data to predict customer revenue trends and support strategic decision-making.

Fast-growing Chinese mobile social e-commerce startup company, also the world's largest. (~50 million sellers in two years, $1.2 billion monthly GMV, 236 million unique visitors per month). Worked in the data analysis department.• Served in the data analysis department, analyzed and built reliable mathematical models to predict buyer and seller growth trends; also identified the characteristics of the top-performing sellers• Analyzed dynamics of digital advertisement and evaluated monetization performances in different product categories• Participated in client meetings to share the findings from data analysis