Shruti G Email & Phone Number

• Proficient in collecting, cleaning, and analyzing data from various sources, utilizing statistical techniques and data visualization tools to identify trends, patterns, and correlations, which resulted in achieving a 25% increase in business goals.• Successfully analyzed large datasets, achieving a 40% increase in operational efficiency and productivity.• Developed impactful Key Performance Indicators (KPIs) and interactive visualizations using Looker Studio 2023, delivering valuable… Show more • Proficient in collecting, cleaning, and analyzing data from various sources, utilizing statistical techniques and data visualization tools to identify trends, patterns, and correlations, which resulted in achieving a 25% increase in business goals.• Successfully analyzed large datasets, achieving a 40% increase in operational efficiency and productivity.• Developed impactful Key Performance Indicators (KPIs) and interactive visualizations using Looker Studio 2023, delivering valuable data-driven insights extracted from BigQuery 2023 that reduced manual reporting by 10 hours weekly, helping leadership analyze and improve data quality.• Effectively collaborated with cross-functional teams, ensuring projects were delivered on time and within budget. Worked closely with data stewards, scientists, and engineers to manage complex data environments like Dynamic Stability Control and SCAV, optimizing data asset management.• Built custom dashboards from scratch according to client requirements using metadata, QlikView 2023, and SQL Server 2019, enhancing data reporting and decision-making processes. Authored advanced SQL queries to retrieve and validate data, showcasing strong problem-solving skills.• Led the development and maintenance of status reporting for data population efforts and supported data governance initiatives. Created comprehensive test plans, cases, and scenarios for A/B testing and User Acceptance Testing (UAT) based on business requirements.• Built a dashboard from scratch as per client requirement using metadata, Tableau 2023, SQL Server 2019, and standard build procedure.• Writing Advanced SQL Server 2019 queries for retrieving and validating the data against different kinds of reports.• Created process flow documents which were used to manage workflows for a 12M product line and created user training materials which reduced manual handholding of clients by 35%. Show less

• Created an aggregated report daily for the client to make investment decisions and help analyze market trends.• Built an internal visualization platform for the clients to view historic data, make comparisons between various issues, analytics for different bonds and market.• The model collects, merges daily data from market providers and applies different cleaning techniques to eliminate bad data points.• Data Retrieval using Confidential Data Studio and PostgreSQL 12.• Built… Show more • Created an aggregated report daily for the client to make investment decisions and help analyze market trends.• Built an internal visualization platform for the clients to view historic data, make comparisons between various issues, analytics for different bonds and market.• The model collects, merges daily data from market providers and applies different cleaning techniques to eliminate bad data points.• Data Retrieval using Confidential Data Studio and PostgreSQL 12.• Built the model on Azure platform using Python 3.7 and Apache Spark 2.4 for the model development and Dash by Plotly 1.13 for visualizations.• Built REST APIs to easily add new analytics or issuers into the model.• Automate different workflows, which are initiated manually with Python 3.7 scripts and Unix shell scripting.• Extensive experience in Text Analytics, generating data visualizations using R 3.6, Python 3.7 and creating dashboards using tools like Tableau 2020.1.• Create, activate and program in Anaconda environment with Python 3.7.• Experience in developing complex Informatica maps and strong in Data Warehousing concepts along with understanding of standard ETL transformation methodologies.• Worked on predictive analytics use-cases using Python 3.7.• Clean data and processed third party spending data into maneuverable deliverables within specific format with Excel macros and Python libraries such as NumPy 1.18, SQL Alchemy 1.3, and matplotlib 3.2.• Used Pandas 1.0 as API to put the data as time series and tabular format for manipulation and retrieval of data. Show less

Relational Data base Design & Statistical Analysis | SSMS, Power BI, Python• Designed a relational database using SQL Server Management Studio (SSMS 17), created a comprehensive data dictionary, and established relational tables to structure the database efficiently.• Understood business objectives to develop models that align with and achieve those goals. Reviewed technical requirements and produced test strategies, scenarios, and cases to validate data in reports and dashboards using… Show more Relational Data base Design & Statistical Analysis | SSMS, Power BI, Python• Designed a relational database using SQL Server Management Studio (SSMS 17), created a comprehensive data dictionary, and established relational tables to structure the database efficiently.• Understood business objectives to develop models that align with and achieve those goals. Reviewed technical requirements and produced test strategies, scenarios, and cases to validate data in reports and dashboards using Power BI (June 2018 Update).• Designed and developed ETL processes to transform raw data, flat files, and Excel spreadsheets into structured SQL databases, ensuring data integrity and accessibility with Python 3.7 and SQL Server Integration Services (SSIS 2017).• Managed large datasets to develop insights and visualizations that support business analysis and decision-making processes using Power BI (June 2018 Update).• Analyzed and optimized existing SQL queries, procedures, functions, and views to improve performance and efficiency using advanced performance tuning methods. Facilitated data extraction, interpretation, and analysis through proficient use of SQL queries in SQL Server 2017. Show less

• Aggregated all available customer information, classifying data into categories such as demographic data (age, gender, education), policy-related data (insurance lines, number of policies), claims data (claim settlement duration), and complaints data.• Conducted analyses using R 3.3.1 and Python 3.5, focusing on demographic data including age, gender, education, marital status, employment status, income, and more.• Managed policy-related data analysis, including insurance lines… Show more • Aggregated all available customer information, classifying data into categories such as demographic data (age, gender, education), policy-related data (insurance lines, number of policies), claims data (claim settlement duration), and complaints data.• Conducted analyses using R 3.3.1 and Python 3.5, focusing on demographic data including age, gender, education, marital status, employment status, income, and more.• Managed policy-related data analysis, including insurance lines, number of policies, household tenure, premium levels, and insured cars using SQL Server 2016.• Analyzed claims data, such as claim settlement duration, and the number of claims filed and denied, utilizing data warehousing techniques in SQL Server 2016.• Handled complaints data, focusing on the number of open and closed complaints, and analyzed survey sentiment data, capturing sentiment scores from past surveys in Excel 2016.• Responsible for building data analysis infrastructure to collect, analyze, and visualize data, employing Tableau 10.0 for data visualization.• Gained expertise as a Data Analyst with a solid understanding of Data Mapping, Data Warehousing (OLTP, OLAP using SQL Server 2016), Data Mining, and Data Governance with a focus on Data Management Services and Quality Assurance.• Performed data elements validation using exploratory data analysis techniques (univariate, bivariate, multivariate analysis) in SPSS 24 and SAS 9.4.• Treated missing values, capped outliers, and addressed anomalies using statistical methods in R 3.3.1.• Conducted variable selection by making use of R-square and VIF values derived from R 3.3.1.• Deployed Machine Learning models, including Logistic Regression and PCA, to predict customer churn, implemented in Python 3.5. Show less

• Developed and implemented predictive models using Natural Language Processing Techniques and machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests (Scikit-learn 0.14), K-means clustering (Scikit-learn 0.14), KNN (Scikit-learn 0.14), PCA (Scikit-learn 0.14) and regularization for data analysis.• Designed and developed Natural Language Processing models for sentiment analysis using Python 2.7.• Applied clustering… Show more • Developed and implemented predictive models using Natural Language Processing Techniques and machine learning algorithms such as linear regression, classification, multivariate regression, Naive Bayes, Random Forests (Scikit-learn 0.14), K-means clustering (Scikit-learn 0.14), KNN (Scikit-learn 0.14), PCA (Scikit-learn 0.14) and regularization for data analysis.• Designed and developed Natural Language Processing models for sentiment analysis using Python 2.7.• Applied clustering algorithms i.e., Hierarchical, K-means with the help of Scikit-learn 0.14 and Scipy 0.12.• Developed visualizations and dashboards using ggplot2 (version 0.9 for R) and Tableau 8.0.• Worked on the development of data warehouse, Data Lake, and ETL systems using relational and non-relational tools like SQL (SQL Server 2012), NoSQL (MongoDB 2.4).• Built and analyzed datasets using R 2.15, SAS 9.3, Matlab R2013a, and Python 2.7.• Participated in all phases of data mining; data collection, data cleaning, developing models, validation, visualization, and performed Gap analysis.• Data Manipulation and Aggregation from different sources using Nexus 9, Toad for Oracle 12.1, Business Objects XI, Powerbase 10 and Smart View 11.1.2.3.• Implemented Agile Methodology for building an internal application.• Good knowledge of Hadoop Architecture (Hadoop 1.0.3) and various components such as HDFS, Job Tracker, Task Tracker, Name Node, Data Node, Secondary Name Node, and Map Reduce concepts.• As Architect delivered various complex OLAP databases/cubes, scorecards, dashboards and reports.• Programmed a utility in Python 2.7 that used multiple packages (Scipy 0.12, Numpy 1.7, Pandas 0.11).• Implemented Classification using supervised algorithms like Logistic Regression, Decision trees, KNN, Naive Bayes (all using Scikit-learn 0.14).• Used Teradata15 utilities such as Fast Export, MLOAD for handling various tasks data migration/ETL from OLTP Source Systems to OLAP Target Systems. Show less