Venkat Yarlagadda Email & Phone Number

Develop and implement advanced statistical models and algorithms to extract insights from complex data sets. Use advanced analytics and machine learning techniques to develop models for classification, regression, clustering, and forecasting. Design and implement data processing pipelines, data integration solutions, and ETL processes to streamline data workflows and improve data quality. Responsible for implementing & deploying the end-to-end pipeline of a prediction model. Understand the architecture proposed by the solution teams and ensure the product team is aligned to that overall solution architecture. Responsible for Providing subject matter expertise and explaining the architecture to product owners, designers, scrum teams and architects to ensure requirements are understood and customer value is delivered. Responsible for Guiding and monitoring the implementation process that involves infusion data ingestion to the centralized repository. Participated in Identifying optimization opportunities for current and future infusion data sources in Azure. Understand the architecture proposed by the solution teams and ensure the Participated in Building a scalable solution with the product leaders. Responsible for Identifying dependencies and tradeoffs against risks. Worked on applying advanced statistical and predictive modeling techniques to data related to the optimization of digital experiences. Responsible for providing on-going tracking and monitoring of performance of statistical models and recommending ongoing improvements to methods and algorithms. Propose innovative ways to look at problems using data mining, statistical analysis, advanced visualizations, and machine learning. Manage the development and deployment of machine learning models in production environments, ensuring that they are scalable and reliable. Develop and maintain best practices, standards, and guidelines for data science and machine learning

Building a text model to find connection with fraud claims. Data was collected from SQL server. General cleaning process was done. Used AWS environment to complete the project. Accessed production SQL database to pull extract for validation with third-party data. Performed data validation between SQL Servers and third-party systems. Worked with big data (10M+ observations of text data). [ SQL, SQLite} Cleaned the text data using different techniques. Integrated with AWS platform environment. Utilized cloud computing resources for model optimization/tuning of hyperparameters, and cross-validation of statistical data science models. Used Pandas, NumPy, Seaborn, Matplotlib, SciKit-learn in Python for developing various machine learning models and utilized algorithms such as Logistic regression, Random Forest, Gradient Boost Decision Tree, and Neural Network. Built and analyzed datasets using Python and R. Applied linear regression in Python and SAS to understand the relationship between different attributes of dataset and causal relationship between them. Applied Exploratory Data Analysis (EDA to analyze datasets to summarize their main characteristics, Performed EDA such as bag of words, K-means and DBSCAN etc.

Developed Recommendation systems, Churn prediction, Customer Life Time Value models. Developed time-series models using ARIMA, SARIMA, and Deep Learning approach with Recurrent Neural Networks and LSTM to forecast and optimize sales and revenue. Automated time-series analysis with Prophet. Developed personalized product recommendations with machine-learning algorithms that used Collaborative filtering to better meet the needs of existing customers and acquire new customers. Created machine-learning algorithm and employed logistic regression, random forest, KNN, SVM, neural network, linear regression, lasso regression and k-means. Developed optimization algorithms for use with data driven models such as with supervised and unsupervised machine learning or reinforcement machine learning. Researched statistical machine-learning methods that included forecasting, supervised learning, classification, and Bayesian methods. Advanced the technical sophistication of solutions through the use of machine learning and other advanced technologies. Performed exploratory data analysis and data visualizations using R and Tableau. Used R, Python, and Spark to develop a variety of models and algorithms for analytic purposes. Performed data integrity checks, data cleaning, exploratory analysis, and feature engineering using R and Python.

Worked with various cross functional Agile teams like Data Analysts, Data Engineers, and Data Scientists that support various business units within the organization to achieve impact Developed & validated supervised & unsupervised learning models, created machine learning and AI models using Pytorch and TensorFlow, and used other statistical techniques to solve business problems. Contributed to the development of a strategy on how to use the model/solution to generate impact. Collaborated with business stakeholders to understand their challenges and develop an analytical solution that creates business impact. Contributed to the effort for team to democratize data by consolidating, integrating, cleansing, and minimally curating operational production system data and external data for insight generation. Performed exploratory analysis using python which includes understanding the key statistical metrics on data columns, determining the granularity of the data, & assessing any related merge keys for combining the data Developed a production, end to end solution for deployment, working closely with Data Engineers Contributed to the development of monitoring framework to ensure the tool is performing as intended. Supported stakeholders after deployment to ensure impact is achieved and contributed to the development and maintenance of best practices. Used fuzzy matching, text mining using NLTK (NLP (part-of-speech taggers, n-gram search, sentiment analysis, WordNet) and data reduction and worked extensively on power BI reports, creating data models, data transformations, used DAX extensively for measuring and modeling. Applied statistical and predictive modeling techniques, such as machine learning, decision trees, probability networks association rules, clustering, regression, GLMs and neural and their application to business decisions networks and used Kusto queries to identify the anomalies and outliers for statistical modeling.

Gathered user requirements, analyzed, and designed software solution based on the requirements. Played critical role in establishing best practices for tableau Reporting by conducting meeting with the clients for gathering reporting requirements; identify the KPI's and developing POC Dashboards and OLAP Cubes. Leveraged Google Cloud Platform Services to process and manage the data from streaming and file-based sources. Worked extensively with Advance analysis Actions, Calculations, Parameters, Background images, Maps, Trend Lines, Statistics, and Log Axes. Groups, hierarchies, sets to create summary level and detailed Reports. Building, publishing customized interactive reports and dashboards, report scheduling using Tableau server. Created Relationships, actions, data blending, filters, parameters, hierarchies, calculated fields, sorting, groupings, live connections, and in -memory in both tableau and excel. Tested Dashboards to ensure data was matching as per the business requirements and if there were any changes in underlying data using python libraries such as pytorch. Created organized, customized analysis and visualized projects and dashboards to present to executive leadership. Experienced with primary NLP functions include text mining, text classification, text analysis, sentiment analysis, word sequencing, stop words interfaces to over larger data corpora and lexical resources. Used Azure AI Cognitive services Content Moderator, protects your brand and users with automated image, text, and video moderation services and human review tools such as thumbnails, read text in images. Created Complicated Calculation Based LOD (Level of detail) feature.