● Predicted case volume with 92% accuracy, enhancing resource allocation and reducing overtime.● Developed and maintained advanced data dashboards using SQL and data visualization tools, reducing turnaround time by 20% and saving labor cost by $71K/month through report automation.● Employed data-driven insights to inform and educate senior executives on ML and data science trends.● Performed NLP analysis on chatbot responses and sales scripts for marketing and sales leadership.

● Manage end-to-end analytics pipeline with skills in DataBase Design, Statistical Modeling, Data Analysis, and Data Mining. Design tools and apps to capture, store, and analyze business and customer data using R and SQL, resulting in $21K in savings with custom dashboard and app development.● Streamline patient health monitoring and reporting for trial partners with an interactive dashboard. Utilize skills in Data Analytics and Data Visualization to centralize crucial health metrics and real-time updates, resulting in savings of $27K in labor costs.● Provide actionable insights to senior management on marketing and pricing strategy by aggregating and analyzing demographic data from various external sources like the CDC. Utilize skills in Data Visualization, Statistical Modeling and Predictive Analytics to identify Top 10 zip codes, resulting in a 70% ads savings on product launch.● Reduce communication gaps between clinical trial partners by scripting programs to automate notification processes, using skills in R (Programming Language) and Git. Result in savings of $45K in labor costs.● Build data pipeline for DNA sequence analysis using AWS EC2 with Python. Leverage skills in Machine Learning, Deep Learning, and TensorFlow to increase capacity to process microbiome data by X10.● Design the user journey and gather customer feedback by developing personalized web app, leveraging skills in Natural Language Processing (NLP) and Artificial Intelligence (AI).

• Utilized Data Science to quantify COVID-19 impact, uncover scalable metrics, and identified 1.2M Q2 at-risk renewal deals to executive leadership to boost customer retention.• Innovatively identified 1.5M at-risk renewals by tracking customer web activities on competitor searches through Data Mining and Predictive Modeling techniques.• Automated dashboards in collaboration with sales, BI, and finance to align renewal metrics and enable CX leadership to manage quarterly renewals over 50M.• Piloted a Machine Learning approach to build an early-warning system to identify churn, resulting in higher customer retention and revenue.

● Identified libraries and blogs as high-performing content that increased conversion probability by 10%, using Data Visualization and Predictive Modeling.● Leveraged Data Science and Statistical Modeling to provide actionable insights to Senior Manager of Marketing Analytics to improve campaign efficiency and content health.● Assisted in building client profiles by aggregating data from Marketo, Salesforce, Adobe Analytics, etc, using Data Mining and Data Analysis techniques.● Uncovered trends in online and offline activities prior to conversion and recommended an investment in video content and webinars to the Marketing Operations VP, using Predictive Analytics.● Proved statistically that video views are milestone activities indicative of conversion, using Data Analytics and Data Visualization.● Assisted in building an Account Scoring Model by conducting exploratory analysis on activity-based attribution, using Machine Learning.

• Conducted data mining analysis on login IDs and email addresses to identify 86% of fraudulent account openings.• Developed statistical and machine learning-based controls to reduce alert volume by 40% using R.• Served as the subject specialist for analytics/machine learning tools, including SAP Predictive Analytics, AWS Sagemaker, and Angoss.• Built client behavioral profiles using database tools such as SQL developer, Cisco DV, and Impala.• Produced regular reports on fraud trends, control performance, and ad hoc reports to enhance fraud prevention efforts.

• Leveraged Data Analysis and Statistical Modeling, and Natural Language Processing (NLP) techniques to identify a 10% revenue growth opportunity by optimizing text on Amazon listings.• Extracted insights and sentiment from partner platforms such as Amazon, Walmart, Wayfair, Target, and Overstock using Data Mining and Sentiment Analysis..• Utilized Data Collection and Analysis on product features, price range, and reviews from Amazon listings to provide optimal feature recommendations to product managers.