Prashast Vaish

Worked on a semester-long supply chain optimization project with AgReliant as part of the MS Business Analytics Industry Practicum at Purdue. Using Python, Tableau, and a two-stage stochastic optimization model, we addressed inefficiencies in intercompany transfers, warehouse-to-customer mapping, and excessive shipments. Built a self-serve Python notebook and interactive dashboards to enable real-time analysis and decision-making. Our insights helped reduce intercompany transfers by ~50%, cut average shipment distance by 3%, and lowered excessive shipments by 5%. Additionally, optimizing safety stock allocation could eliminate ~304 truck trips, directly improving operational efficiency and customer satisfaction.

Guided 250+ undergraduate and graduate Business Analytics students through the fundamentals and applications of database systems. Helped students understand ER diagrams, data normalization, and relational database design. Delivered hands-on support with SQL for data manipulation, querying, and analytics use cases. Fostered student engagement through lab sessions, office hours, and project mentoring.

Supported Purdue’s partnership with the Government of the Dominican Republic to develop a national microelectronics workforce development roadmap. Contributed to Phase I by conducting desk research, stakeholder mapping, and initial diagnostics on talent gaps and institutional readiness. Helped shape the strategy for in-country assessments by identifying key challenges and collaboration opportunities. The work laid the foundation for national-level workforce reforms in the advanced manufacturing and semiconductor sectors.

- Customer Segmentation: Designed a segmentation system on Redshift using SQL & Tableau to tier users and performed A/B test to evaluate the impact of targeted incentive strategies, resulting in a 2% improvement in retention & margins - Optimizing Recovery Period: Identified ineffective marketing channels and factors influencing user acquisition through funnel and churn analysis using Redshift, Athena, Tableau, & Excel, leading to a 15% reduction in advertising costs, a 2% improvement in acquisition funnel, and a 20% decrease in user recovery period - A/B Testing on User Conversion: Mapped journey of High-Value Players on SQL, Excel & Tableau to identify potential high-value players & designed an A/B experiment to test a new reward system, resulting in 3.5% increase in player conversion - Compliance and Financial Reporting: Partnered with the Tax & Compliance team to streamline audit reporting using Python & Tableau, reducing audit time by 50%, and analyzed user journey using Redshift to identify optimal KYC integration points, minimizing drop-offs and enhancing compliance, resulting in a $1.5M monthly reduction in tax exposure

- E-Commerce Analytics: Spearheaded a team of 5 to develop a Power BI dashboard, consolidating data from Snowflake, Databricks, and Adobe Analytics to track KPIs across e-commerce journey; Automated ELT processes using Python and Snowflake tasks, enabling insights into inventory, pricing, and customer satisfaction, resulting in $90M increase in online sales - Customer Sentiment Analysis (NLP): Developed an NLP-based multiclass sentiment analysis model using a Stacked Bi-Directional LSTM with Attention in Python, achieving an 85% F1 score to analyze customer satisfaction post order fulfillment - SEO through Web Analytics: Led a 3-member team to leverage Adobe Analytics Clickstream data to develop key metrics & evaluate search efficacy, enabling SEO optimization and strategic solutions, resulting in a 13% increase in search success rate

- Data Warehousing for Analytics: Created Claim Centre data marts by implementing dimensional modeling and ELT processes using Redshift, reducing data refresh time by 30%. Developed ~50 KPI-focused Tableau reports to streamline performance tracking and Excess monitoring for motor insurance, improving decision-making and operational efficiency - Claim Centre Performance Evaluation: Developed ThoughtSpot reports with ~50 KPIs, enabling the Motor Claim Services and Internal Settlement teams to evaluate consultant performance and track excess amounts collected, waived, or abandoned - Data Migration: Built a migration remediation strategy for Australia’s largest insurer post-Life insurance divestment, avoiding litigation with ASIC and APRA by completing data source remediation ahead of deadlines

Learned about the basics of data analytics and how businesses use tools like SQL and Python to solve business problems, generate insights, and make key decisions. Also learned about data visualization and its importance in storytelling

At Purdue, I engaged in a rigorous curriculum covering data science, machine learning, statistical modeling, and business strategy. I applied analytical techniques to real-world problems through hands-on projects and industry practicums, collaborating with peers and industry mentors. My experience also included teaching assistantship roles, leadership in data-driven initiatives, and participation in analytics competitions, all of which deepened my understanding of using data to drive business decisions.

During my undergraduate studies at SRM, I built a strong foundation in computer science and IT, exploring areas such as data structures, algorithms, database systems, and web technologies. I actively participated in coding competitions, technical fests, and group projects that honed my problem-solving and teamwork skills. My time at SRM sparked my interest in analytics and set the stage for my journey into data-driven decision-making and business insights.