I contribute to optimizing pricing strategies for holiday and airline business by analyzing market trends, demand patterns, and competitive data. Collaborating with cross-functional teams, I ensure revenue growth and customer satisfaction through effective pricing models, testing and innovative solutions. My role focuses on leveraging data insights and industry expertise to drive profitability and operational efficiency.

As a Data Scientist, I specialize in leveraging advanced data analysis, machine learning and modeling techniques to drive meaningful business solutions. My role encompasses two key areas:1. Credit Card Profit Score (CCPS) Development:I have built sophisticated models that accurately assess and provide the profitability of credit card accounts, helping businesses optimize their portfolio and improve customer targeting. Complete Tech Stack - Credit Card Analytics, Big Data, pySpark, SAS, Machine Learning, Python, SQL.2. Personalization Engine - Channel Propensity Model:Designed and implemented personalized recommendation systems, tailoring communication and marketing strategies to individual customer preferences and behaviors, enhancing user experience and boosting engagement. Complete Tech Stack - Marketing Analytics, Big Data, Hadoop, pySpark, Spark SQL, Python, Machine Learning, Deep Learning, Stakeholder Management.These responsibilities allow me to apply my expertise in data science and business intelligence to create measurable value for my organization.

As a Data Scientist at Ecolab, a global leader in water, hygiene, and energy technologies, I played a key role in driving projects that combined advanced data science with Ecolab's dedication to sustainability and innovation.Key Projects:1. Predictive Maintenance for Industrial RO Membranes: I developed a machine learning model that accurately forecasts the remaining useful life of industrial reverse osmosis (RO) membranes by analyzing sensor data. This innovative solution is expected to generate approximately $2.2 million in additional annual revenue, reducing maintenance costs and minimizing downtime.2. Legionella Bacteria Prediction in Cooling Towers: I created a predictive solution to monitor and forecast the growth of Legionella bacteria in cooling towers. This complex solution combines data from Laboratory Information Management Systems (LIMS), sensor data, environmental conditions, and Python-based machine learning techniques. This project has directly contributed to a revenue increase of $14 million while improving public health and safety.Through these projects, I demonstrated my ability to apply data science to real-world challenges, resulting in significant revenue growth and promoting sustainability. My work at Ecolab reflects the power of data-driven insights in enhancing operational efficiency and advancing environmental goals.

Projects1. PCB (Parts & Component Business) physical, system mapping & CBM (cubic meter) analysis- worked with logistics, production, and IT dept. Part by part cubic meter calculation for a packaging cost analysis to prepare an outbound logistic budget at a kit level.2. Jigs & fixtures costing model for VW PT (Production Technique) Business considering all the expenses.3. Direct material tact-wise consumption of engine for WIP valuation purpose- worked with direct material & inbound logistic controlling production dept. for total cumulative cost analysis at tact level.4. Major consumables consumption at paint shop for budget preparation and volume dependency purpose by analyzing the frequency and tact wise consumption.5. Cost driver identification of production areas, understanding consumption trends, and analyzing usage of material. Key Result Areas: 1. Calculating inbound logistic cost, custom duty, and anti-dumping duties of direct car parts required for business case evaluation, debit and checklist purpose, aftermarket spares pricing, etc.2. Provide and discuss the logistic cost, custom duty on the part to the part basis for possible saving.3. Monitoring - controlling of scrap cost by production, planning area hence preparing scrap JV to ensure the correct cost center, GL accounting.4. Monthly closing of overhead, direct-indirect labor, inbound logistic cost thus preparing various P&L items for plant cost and headcount report (Actual/Forecast vs. Adjusted budget).5. Preparation of special reports and inventory spare reports for analysis of current expenses.6. Accurate forecasting of future costs after discussion with CC to anticipate any cost overrun/saving.7. Review the total part cost (landed cost) for aftermarket spares, PCB, and Engine business.8. Custom duty change impact on total part cost.Learnings: 1. Product/Business-wise (Spartenrechnung) cost allocation concept.2. Major cost driver’s in the production area. 3. JIT & Kanban concept.

Project: Nitration of O-xylene As the nitration of o-xylene uses mixed acid in conventional method. Mixed acid is hazards and harmful in many ways so need to minimize the usage, by bringing varies catalyst and can make the reaction environmental friendly. Generally nitration reactions are carried out in batches, can be turned into continuous operation and make it economical and more efficient.