Jan 2024 – Sep 2024 · 8 mos

• SE performance Analysis
• Collaborated with a Fortune 100 Travel and Hospitality client to enhance sales agent performance by identifying and implementing traits of top performers.
• Addressed disparities in sales executive metrics, such as Volume per Guest (VPG) and Close Rate, through exploratory analysis and developed regression and XGBoost models to improve performance.
• Utilized SQL for data extraction and Python for data manipulation, model development, employing regression and tree-based models to identify key features and applied gradient descent to formulate an Expected VPG calculation.
• Used various statistical testing methods( Mann Whitney U ,Chi Square , etc.) for tests of fairness for the expected VPG calculation
• Bad Goods Reduction
• Worked on methodology to optimize inventory management by developing a mechanism for suggesting supply adjustments.
• Ideated an automated system to address long-term salvage risk by analyzing root causes and providing actionable insights.
• Developed supply move recommendations with predefined thresholds and matrices. Leveraged PySpark and SKU segmentation for forecasting.
• Resulted in a net increase in Customer Order Fulfillment (COF) by 3%.

Jan 2022 – Dec 2023 · 1 yr 11 mos

• Real Estate Analysis
• Estimated market potential for a leading Container and Ground level office supplier in the construction segment.
• Analyzed Sales Rep Performance, classified High/Medium/Low value projects/volume products, aiding sales strategy optimization.
• Delivered ad hoc analyses as per clients' requirements for sales strategy optimization using Python, Tableau, Salesforce, and Forecasting.
• These ad hoc analyses resulted in savings of ~$500 k for the client in labor management, contributing to a ~$1mn increase in total revenue.
• Product Pricing optimization
• Contributed to a 4-member team in a fast-track project to create marketing regions using a 2-step clustering approach using K-Means , supporting the sales team in optimizing product pricing.
• Successfully implemented methodology by curating data and running models for clustering using Python libraries Sklearn, pandas.
• Order Cancellations predictions
• Performed a POC to provide client visibility on order cancellations at different timeframes, demonstrating the relationship between customer/site behavior, climatic conditions, and cancellation risks to aid labor and logistics.
• Developed multiple classification models for different quantity buckets, aiming to enable plant managers to track scheduled and
• forecasted quantity for optimized labor and material requirements.
• Projected cost savings in annual labor were over $2mn+
• Commercial Services
• Part of a Rapid Insights Team for a leading CPG Client, where Insights resulted in ~2% enhancement in Strategic Business Objective(SBO) Metrics in verticals such as Accounts Payables and Receivables.
• Collaborated closely with the client to identify visibility opportunities using Tableau Dashboards and SQL for data extraction

Sep 2021 – Dec 2021 · 3 mos

• War On Waste
• Assisted a Fortune 500 CPG major in constructing a tool and delivering interactive dashboards, leading to a reduction in excess/obsolete inventory and subsequent wastage.
• Developed front-end and back-end codes for presenting perishable goods data, contributing to an ~8% decrease in inventory wastage through Python and Tableau analysis, focusing on identified pain areas after dashboard development

Jul 2020 – Sep 2021 · 1 yr 2 mos · Pune, Maharashtra, India

• Claims Denial Prediction Model using Python:
• Predicted the claims that are likely to get denied during payment for multiple US Healthcare provider clients.
• Developed various regression and classification algorithms by using various Sklearn libraries such as Linear Regression, Decision Trees, Random Forest, CatBoost, XGBoost, Naïve Baye’s
• Developed interactive dashboards using RShiny.
• Reduced the rate of inaccuracy to meet SLAs and avoid penalties.
• Increased the Revenue Collection by 5% .

Jul 2019 – Jun 2020 · 11 mos · Pune, Maharashtra, India

• Collaborated with analytics teams to create business solutions for clients by extensively working on data analysis.
• Participated in data pre-processing techniques in order to make data useful for creating Machine Learning Models.
• Developed tools for the deployment of the Machine Learning solutions using packages and frameworks like Flask, and Shiny .

Feb 2019 – Apr 2019 · 2 mos · Pune/Pimpri-Chinchwad Area