Dipanshu Singh

- Built the outbound acquisition program from scratch: three A/B-tested email sequences with reply playbooks for qualification and objections; also ran outbound for a US client - Built a 90-day KPI tracker (219 live formulas) covering stage conversion, deliverability flags and cash flow; used it to find the main funnel leak and add a qualification gate

- Supported daily operations in a high-volume service environment using data tracking and performance metrics. - Analyzed sales patterns, peak-hour demand, and customer behavior to support operational efficiency. - Worked in a fast-paced, multilingual environment, strengthening communication and execution under pressure. - Balanced part-time work alongside a full-time Master’s in Data Science & AI.

- Led 40+ enterprise software implementations across clients including Acer, Apple, Godrej, and Reliance. - Built Qlik Sense BI dashboards to track adoption, usage trends, and client health metrics. - Increased product usage by ~10% QoQ through data-driven reviews and CRM strategy. - Reduced churn risk by translating analytics into actionable client recommendations. - Generated ~15% quarterly revenue uplift through custom development and feature optimization. - Collaborated with engineering teams to improve

- Analyzed technical specifications and pricing data for 100+ industrial products. - Supported tender evaluation and quotation creation for 20+ enterprise clients (e.g., Siemens, Adani Group). - Worked with structured and semi-structured data to assist procurement and decision-making. - Gained early exposure to data-driven business processes and technical documentation workflows.

- Tracked inventory and demand data for 20+ SKUs in a high-volume warehouse environment. - Supported fulfillment operations impacting 250+ product deliveries across 100+ customers. - Observed and analyzed end-to-end supply chain workflows, from inbound logistics to last-mile delivery. - Built a foundational understanding of operations data, efficiency metrics, and scalability challenges.

Tracked inventory and demand data for 20+ SKUs in a high-volume warehouse environment. Supported fulfillment operations and analyzed workflows from inbound logistics through last-mile delivery.

Focus Areas: - Machine Learning & Predictive Analytics - Deep Learning & Neural Networks - Natural Language Processing (NLP) - Data Engineering & End-to-End Data Pipelines - AI-Driven Decision Systems & Business Analytics - Hands-on projects involving data preprocessing, modeling, evaluation, and i

- Strong foundation in mathematics, statistics, and problem-solving - Developed analytical thinking and system-level reasoning applicable to data science