Kumari Shivani

FSD Pricing Analyst : Developed and Implemented Pricing Strategies: Designed and implemented fixed pricing structures for articles from suppliers, optimizing pricing to ensure a competitive edge and achieve targeted profit margins. Analyzed market data to adjust pricing strategies in response to competition, resulting in a 15% increase in profit margins. Sustainable Sourcing Coordinator: Led Zero Carbon Footprint Initiative. Spearheaded a project aimed at achieving a zero carbon footprint within 5 years. Evaluated and verified the sustainability of organic products by assessing deforestation impacts and tracking the geographic origins of sourced materials. Utilized geolocation data to analyze sourcing practices, contributing to a 20% reduction in carbon emissions from product sourcing. Finance Chabot Specialist: Enhanced Financial Insights with AI. Developed a chatbots specialized in providing detailed and accurate financial information about Metro Group's performance, financial statements, and related topics. Improved user engagement by 25% and reduced response time for financial queries by 40%, facilitating better navigation of Metro Group's financial landscape.

• FES - GET - Model solution drives operational efficiencies by reduction in volume of low value alerts without the loss of risk coverage. The model uses ML technique - XG-Boost probability approach to detect all false alarm and hibernate it. The model has provided an edge of ~60%.
• Alert Hibernation – The approach aims at reducing the false alert further by adding the final evaluation layer. NLP Algorithm such as Word Embedding and Cosine Similarity and Clustering to achieve next 15% of reduction of false alert trigger volumes.
• Name Screening - It is a real time system where the transactions are passed and matched against the Watch list. The model Linguistic predictor approach such as threat indicators, Numeric patterns, Name-Geo Patterns and causal patterns to raise alerts.
• Resume Parser - The Project uses NLP technique such as NER, Spacy resume summarizer and regex pattern match to extract information of resume for easier evaluation of applicants.

• Cataract Detector: Classification model build using OpenCV/CNN approach to help doctors with quicker classification and diagnosis.
• Twitter Dashboard: Used Sentiment analysis to study the market presence of the organization and competitive market.
• Go-Live Delay Prediction: The model used ML technique -Logistic model approach to see if the solution deployment can be done on time to avoid the late charges and factors affecting and pain points where reported by n-gram occurrence method.
• Call volume Forecasting and Staffing Model: Predicting alarms input for each hour of operation to provide optimal staffing using ARIMAX techniques.

• Loan Bucket Analysis: ML technique multi-class classification XG-Boost used check if the loan amount applied is within the repaying capacity and suggesting the loan capping amount for an individual.
• Debt Recovery: Model using sandwich ML techniques of Clustering and LGBM prediction used to access of the risks loan buyouts and amount that can to proposed while raising the bid.

• PM-Certy: Followed up with clients to verify optimal customer satisfaction following support engagement and problem resolution.

• Market Analysis: Used Market Mix Models, Segmentation and Clustering to analyze the market to find correct pitch of different segment of people.