Priyanka Das

AI-Powered Lead to Cash (L2C) Agents, Client: Water, hygiene, and infection prevention solutions provider, Duration: 6 months (ongoing).

• Led the end-to-end design and development of AI-powered Lead-to-Cash (L2C) agents to automate the order-to-invoice lifecycle.
• Defined functional and technical requirements to align automation outcomes with commercial goals.
• Coordinated cross-functional collaboration between data engineering, QA, and delivery teams for scalable implementation.
• Integrated AI-based predictive error detection and remediation frameworks to improve process accuracy and speed.
• Delivered an intelligent automation system, enhancing cash flow visibility and order management efficiency.

Gen AI–Powered Supply Chain Intelligent Response: Client: Leading global clothing and apparel company; Duration: 9 months.

• Developed AI-driven dashboards for real-time visibility across supply chain operations, including logistics, production, and distribution.
• Built predictive models for demand forecasting, logistics exception management, and stock-out prediction.
• Integrated a Gen AI conversational assistant to deliver anomaly alerts and actionable insights in real time.
• Enabled proactive risk management and data-driven decision-making, improving forecast accuracy, and operational agility.
• Supported supply chain planners in scenario analysis and performance optimization.

AI/ML Master Data Quality Framework, Client: Global pharmaceutical and vaccines manufacturer, Duration: 12 months.

• Developed AI/ML models to identify and correct missing, inconsistent, or inaccurate supply chain master data.
• Designed and deployed Azure ML pipelines using algorithms such as Random Forest, XGBoost, and LightGBM for high accuracy data validation.
• Created custom AutoML workflows for automated data transformation, quality enhancement, and validation.
• Implemented an agentic framework enabling automated, self-healing data corrections to reduce manual interventions.
• Delivered a robust, scalable AI solution that improved master data accuracy, reporting timeliness, and decision reliability across global supply chain operations.

Location Intelligence, Client: Leading U.S. Jewelry and specialty retailer. Duration: 4 months.

• Conducted market and spatial analysis to identify optimal locations for expansion, relocation, or consolidation.
• Built AI/ML models to simulate incremental revenue and profitability impact for new or repositioned stores.
• Created interactive dashboards integrating demographic, regional, and store performance data.
• Partnered with business and strategy teams to support data-backed investment planning and portfolio optimization.
• Delivered insights that improved market reach, sales performance, and network efficiency.

Store 360, Client: Global petroleum and energy company, Duration: 6 months.

• Designed and implemented a Store 360 analytics platform to deliver a unified view of retail operations across locations, channels, and categories.
• Conducted advanced analytics on sales, customer behavior, and inventory to identify performance drivers.
• Built executive dashboards and KPIs, enabling real-time decision-making and operational transparency.
• Delivered data-driven insights supporting strategic portfolio planning, merchandising optimization, and profitability growth.
• Developed custom dashboards and visualizations to effectively communicate insights from complex data sets, resulting in actionable recommendations.

Merchandising and Revenue Management Analytics, Client: Global FMCG and Beverage Leader, Duration: 2.5 years.

• Designed and implemented merchandising and pricing analytics dashboards (Brand Ladders, Candlelight, Pack Size Curve, Heuristic Score Card) across multiple regions.
• Acted as both a Business Analyst and Developer, translating commercial goals into analytical models and visual dashboards.
• Built price elasticity, promotion impact, and optimization models to guide pricing and assortment decisions.
• Developed pricing simulators to evaluate discount structures, bundling options, and promotional event strategies.
• Delivered data-driven merchandising insights that improved revenue, profit margins, and category-level performance.

Text mining on assembly data. Client: Global heavy machinery and equipment manufacturer. Duration: 8 months

• Developed AI-driven text mining and machine learning models to identify faulty machinery parts and determine their product codes from assembly line data.
• Conducted deep statistical analysis for complex algorithm development, enabling accurate fault classification.
• Implemented automated fault detection and product mapping, improving traceability, and reducing manual investigation time.
• Optimized inventory management and warranty renewal processes through precise defect identification and predictive insights.
• Delivered a data-centric solution that improved maintenance efficiency, reduced warranty costs, and enhanced operational reliability.