- Spearheaded the development of a predictive AI model in Python, leveraging libraries such as NumPy, TensorFlow, and Pandas to analyze and predict potential clients based on diverse pipeline factors
- Employed advanced statistical techniques to extract meaningful insights from raw data and reduced data preprocessing time by 20% and accelerated model training cycles by executing seamless data manipulation
- Implemented machine learning algorithms to analyze annual revenue, company location, industry types, employee count, and several other factors optimizing the model for accurate sales targeting
- Achieved a 20% improvement in prediction accuracy compared to traditional methods
P
Purdue University
RESIDENT ASSISTANT
Hammond, IN, US
October 2022 - May 2023
SOFTWARE ENGINEER Nividous Software Solution
Gujarat, IN-GJ, IN
April 2020 - August 2022
Skills
Agile MethodologyAlgorithmsAmazon Web ServicesArtificial IntelligenceAsanaAutomationBackup DevicesBusiness IntelligenceCarrying out AssessmentsCloud ComputingCommunity ManagementComputer ProgrammingComputer VisionContinuous IntegrationCoordination SkillsC++ (Programming Language)Customer Demand PlanningData AnalysisData ArchitectureDatabasesData Entry SkillsData ProcessingData SecurityData Storage TechnologiesDecision Making SkillsDeep LearningDistributed SystemsEnterprise Resource PlanningEvent ManagementForecasting SkillsGoogle AdSenseJIRAKnowledge of EngineeringKnowledge of StatisticsLearning Management SystemsMachine LearningMedical Billing and CodingMicrosoft ExcelMicrosoft OfficeMicrosoft Office SpecialistMicrosoft SQL ServerMySQLNatural Language UnderstandingNeuroscienceNumPyOffice SuiteOracle ApplicationsOracle ErpOracle SQL DeveloperPandasPower BIPredictive Data AnalysisProblem SolvingProgramming LanguagesProject ManagementPython (Programming Language)PytorchRaw DataRobotic Automation SoftwareSalesSalesforce.ComShoppingSocial MediaSoftware EngineeringSQL DatabasesStreamlineSystems DesignSystems ImplementationsTableau (Software)Technical SkillsTensorflowWorkflows