- Analyzed large-scale financial transaction datasets using Amazon Redshift, employing anomaly detection and clustering to identify and mitigate fraudulent activities, increasing fraud detection efficiency by 20
- Built predictive models using machine learning techniques including random forests and ARIMA for time series forecasting to predict revenue fluctuations, customer churn, and anomalies, improving forecast accuracy by 10
- Provided real-time analytics and reports on financial performance metrics, utilizing data mining and pattern recognition to optimize resource allocation and minimize financial risks, enabling informed strategic decisions
S
Sphere Rays Technolabs
Data Analyst Intern
Gujarat, IN-GJ, IN
October 2021 - April 2022
Skills
AccountingAirflowAlgorithmsAmazon RedshiftAmazon Web ServicesAnomaly DetectionApache HadoopApache HTTP ServerApache SparkAttention to DetailAuditing SkillsBig DataBudgeting SkillsBusiness Process ImprovementCluster AnalysisCommunication SkillsData AnalysisDatabase AdministrationData CleansingData IntegrityData MiningData QualityData ScienceData VisualizationData WarehousingExtract Transform Load (ETL)Feature EngineeringFinancial AnalysisForecasting SkillsFraud Prevention and DetectionFriendlinessGitHealth CareJupyterKaggleKey Performance IndicatorsKnowledge of FinanceKnowledge of MathematicsKnowledge of StatisticsLogistic RegressionMachine LearningMATLABMetricsMicrosoft ExcelMoney InvestmentsMongoDBMySQLNormalization ProcessesNoSQLNumPyOutliersPandasPattern RecognitionPerformance ManagementPL-SQLPostgreSQLPower BIPredictive Data AnalysisPredictive ModellingProgramming LanguagesProject ManagementPython (Programming Language)Quality ManagementRandom ForestReliabilityResource AllocationRisk AnalysisRisk FactorsSnowflakeSQL DatabasesSuccess Driven PersonTableau (Software)Team WorkingTime SeriesTransaction DataVisualizationWorkflowsXgboost