Collaborated with people across the globe to provide high-impact machine learning solutions and executive-level dashboards for clients in retail, industrial goods, Oil and gas, and public sector domains• Developed an Advanced Analytics optimizer to define the best yield mix for a gas and oil refinery given their operational, business constraints, and relevant product prices to generate $20M benefit• Designed & implemented personalization & email optimization for a sports retailer by conducting large scale data enrichment and customer segmentation to generate ~6% revenue uplift and ~14% email engagement uplift• Enabled data-driven decisions by developing a tableau dashboard to easily analyze survey data for a public university in the US• Developed corporate pricing tool using Plotly dash for a European bank to assist the RMs to negotiate the optimal spread to offer to client's basis multiple aspects. The tool catered to multiple product lines including loans, trade, and cash management• Conducted conferences and training sessions to upskill new joiners and non-analytic peers in the company in data visualization and analytics toolsAnalytical Tools: SQL, Python, R, Alteryx, MS Excel, Excel VBA, Hive, PySparkData Visualization Tools: Tableau, R Shiny, Plotly Dash, CSS, JavaScript, PowerBIAlgorithms: K-means Clustering, Regression Models (Multilinear, Logistics, Random forest, Regularization)Analytical cloud platforms: Google Cloud Platform

Generated actionable insights for a Fortune 100 retail giant by utilizing SQL, Tableau, R Shiny, Python, Alteryx, and machine learning algorithms and Managed multiple ad hoc requests raised by stakeholders using the ticketing platform JIRA • Built pipelines for capturing a customer’s past behavior in terms of revenue, liquidity, seasonality, average basket size, demographics (geography, ethnicity, income group, and age group), average order value, recency (how recent has he placed an order) and clickstream activity to predict a customer’s lifetime value (CLTV) in the next 12 months using Random Forest Algorithm. • Interacted with different stakeholders, understanding their general requirements, and created automated analytical frameworks on Tableau using Hive and python scripts as a one-stop solution for most of the requirements• Designed a widely consumed dashboard in tableau to capture the gamut of supply chain KPIs after collaborating with various stakeholders, which helped clients gauge performance over time. The success of the pilot project led to further expansion of the team to 5 members• Developed curriculum and case studies for teaching new joiners; helped with recruitment drives by creating case studies and conducting interviews