• Developed a generative AI-based solutions that included a support ticket chatbot, RAG-based document query, document summarizer, and a sentiment classifier for customer reviews, resulting more than 15% increase in productivity in various departments and increase in customer satisfaction.• Utilized SQL to query and extract data from various databases, including TablePlus and Amazon RedShift, to support data analysis and reporting efforts reporting effectiveness.• Developed and optimized SQL queries on Amazon RedShift for efficient data retrieval and analysis, resulting in a 20% improvement in data retrieval speed and scalability.• Automated backend data ingestion processes into Tableau to streamline data integration and enhance data visualization capabilities, resulting in a 30% reduction in time spent on data preparation. • Leveraged Tableau to create interactive and insightful data visualizations, providing stakeholders with actionable insights and facilitating data-driven decision-making.• Utilized Tableau Prep Builder to preprocess and clean data, ensuring data quality and consistency for analysis and reporting purposes.• Collaborated with cross-functional teams to gather requirements, define project objectives, and develop analytical solutions to address business needs and challenges.

• Conceived and led the creation of predictive maintenance machine learning models, which reduced maintenance costs and downtime by 25%.• In order to increase customer engagement by 20%, I oversaw a team of data scientists and engineers to develop AI-powered solutions for customer segmentation and tailored marketing.• Acted as a Data Scientist in a highly technical and analytical role, harnessing a passion for data, mathematics, programming, and statistics.• Mined, aggregated, and analyzed data to provide predictive insights and outcomes for key drivers of the business.• Collaborated with business stakeholders, utilizing analytics to drive change and improvement.• Identified opportunities for organic growth, new store site selection, operational efficiencies, marketing strategies, and overall profitability.• Cleaned text data using various techniques to ensure its quality and suitability for analysis. and involved in implementing methods such as text preprocessing, removal of noise and irrelevant information, and handling missing data.• Gained insights from the text data, conducted exploratory data analysis (EDA) using techniques like Bag of Words, K-means clustering, and DBSCAN and identified patterns, grouped similar data points, and extracted meaningful information from the text corpus.• Identified and experimented with different embedding techniques including Google Universal Sentence Encoder, DocToVec, TF-IDF, BERT, and ELMO.• Developed a culture of ongoing learning and professional development by mentoring and coaching junior team members.• Developed and implemented AI algorithms and models to solve complex problems, resulting in a 25% improvement in accuracy and efficiency of data analysis.• Designed and developed an AI-based web application that increased user engagement by 30% and reduced customer churn by 20%.

Automated manual processes with AI, enhanced data quality through data cleaning and preprocessing, and contributed to innovative projects in image classification and text classification.• Designed and developed machine learning models that optimized inventory management, resulting in a 20% reduction in stockouts and a 15% increase in inventory turnover.• Developed and maintained AI-based systems and applications, improving operational efficiency by automating manual processes and reducing errors by 25%.• Developed and maintained AI-based databases, implementing data cleaning and preprocessing techniques that improved data quality by 30% and reduced data retrieval time by 40%.• Created an image classification model that compares actual and expected solar plant performance and identifies the driver of any deviation in those two metrics. While it could be handled as a time-series problem, the values are processed into a graph and identified using computer vision Al that is more than 93% accurate. • Worked on digitizing handwritten stickies. Stickies written during agile design thinking sessions were digitized using handwriting recognition technologies using Convolutional Neural Networks and Word Beam Search. • Contributed to the development of a text-classifier using pre-trained BERT model.

• Developed and maintained AI-based cloud applications, enabling seamless integration with existing systems and reducing infrastructure costs by 20%.• Developed and maintained AI-based APIs, facilitating data exchange between different applications and improving system interoperability by 25%.• Developed and maintained AI-based mobile applications, resulting in a 20% increase in user downloads and a 15% improvement in user ratings.• Communicated Results to Decision Makers, explain the Results and conclusions of the analytics process in both written and oral presentation formats.• Create recommendations that are practical, actionable, and have material impact, in addition to being well-supported by analytical models and data.• Developed and operationalized an object detection model to detect customer-owned, in-ground swimming pools. Identifying this subset of customers helps the Smart Saver Program target them for promotional marketing initiatives.• Extracted line items- SKU, Item Description, Item Cost, Tax, Date, Vendor from scanned receipts. Conducted data mining and analysis to identify patterns and trends, enabling predictive modeling for business outcomes optimization.• Analyzed system efficiency and identified bottlenecks, proposing automation opportunities to improve productivity and sustainability.• Built and supported a community of Citizen Data Scientists within the organization, organizing workshops and training sessions to promote data literacy.• Conducted data mining and analysis to identify patterns and trends, enabling predictive modeling for business outcomes optimization.• Led the deployment of AI/ML models from development to production, ensuring rigorous validation and testing protocols were followed.• Deployed, managed, and scaled the containerized model using Azure Kubernetes Service (AKS) with load balancers to ensure efficient traffic distribution and high availability.

• Develop, test, validate and refine predictive models using artificial intelligence, deep learning and machine learning to optimize customer experiences, revenue generation, operationaleffectiveness, marketing success and other business outcomes. Lead Project Teams and participated in multidisciplinary analytics project teams.• Partner with Business Clients, establish professional relationship, and communicate with analytics clients in order to understand business needs.• Frame Problems with Stakeholder, research and construct problem frames in order to understand the analysis context and scope that will provide timely, useful results.• Interview Subject Matter Experts, plan and conduct individual interviews with experts to gain valid information and data needed for analysis. Designed, developed, and maintained business intelligence software/platforms.• Evaluated the performance of each embedder, determined the optimal choice that yielded the best results in terms of matching user inputs with trained questions, and associated them with the corresponding department. • Developed quality reporting tools to extract data from multiple sources, providing proactive trend analysis and expected business outcomes.• Gained a deep understanding of operational processes and needs, using technology to deliver impactful solutions.• Established protocols, methods, and systems to collect, aggregate, store, and analyze data.• Constructed dashboards and other visualization tools for easy data consumption by stakeholders and end-users. Created ad hoc reporting and analysis to support the organizational hierarchy.• Investigated market demographics and competitor landscape, suggesting offensive/defensive marketing tactics.• Utilized Geographic Information System (GIS) technology to evaluate member plotting, drive time reach, and market penetration potential.

• Assisted Finance in debugging and improving Settlement Payment Reports for cash reconciliation. Developed Periscope dashboards for internal reporting and use.• Worked in cross-functional teams with enthusiasts for our customers' experiences across platforms and partners, including data scientists, user researchers, product managers, designers, and engineers.• Conducted analysis on large datasets to derive valuable insights into user behavior that influenced decisions about products and designs.• Feature Tools and the Pandas Python package to work on data cleansing, analytics, and model feature engineering. Utilized AWS Sage Maker ML Ops tools.• In order to provide a response label to each article for subsequent categorization, we updated our Python scripts to match training data with our database stored in AWS Cloud Search.• Completed analytics for supply chain reporting and used Power BI.• Built, trained, and deployed machine learning models using Amazon Sage Maker.• Designed and implemented a CI/CD pipeline to automate model development and model deployment. Developed a customer service chatbot using generative AI.• Setup model training pipelines to be triggered when model drift is detected.

• Collected information from many sources, such as sales exchanges, client communications, product stock, and advertising initiatives. Constructed forecasting models for Amway product sales. Divided Amway's clientele to comprehend distinct types of customers.• Used EDA to get a basic comprehension of the information. Determine which data quality problems exist and, if necessary, clean up the data. Examined demographics, buying history, and customer behavior.• Enhanced model deployment efficiency and optimized data processing by utilizing R and Scala. Constructed summary statistics and data visualizations to investigate patterns and trends• Employed machine learning and time-series analysis methods to forecast future sales patterns.• Created and executed Media Mix Models (MMM) utilizing statistical programs like SAS and R to determine the best marketing avenues for increasing revenue. Developed a recommendation engine that suggests relevant products to customers.• Implemented personalization techniques to enhance the shopping experience.• Based on description (text) of a work request, recommend the most similar work order done in the past. Planners use the tasks from the most similar work orders to plan for the new work request. Used entity recognition system to extract them.• Did all the work-initial business research, model development, microservice development, API development, deployment, scaling and feedback loop to train the model automatically on user feedbacks.• As part of the operations team, wrapped models created by myself and other data scientists in the team using flask app and deployed them to Pivotal Cloud Foundry, Kubernetes, AWS and Azure using Docker Containers. Used dependency parser to group the relevant entities together.