● Open to Work

Prateek Pagare

Data Scientist
Seattle, WA

About Me

Improved demand forecasting accuracy by 15% through trend and scenario analysis, and outperformed volume prediction baselines by 14.5–20.6% with Bayesian–ARIMA optimization across 275 time series with full uncertainty quantification. Evaluated 8+ GenAI and Agentic AI architectures for production workflow automation. MS Data Science from Seattle University with applied experience in forecasting, model optimization, explainable analytics, and building automated data pipelines in Python, R, and SQL.

Skills & Technologies

PythonRSQLNumPyPandasScikit-learnTensorFlow/KerasXGBooststatsmodelsPyMCHuggingFaceSciPyStatistical ModelingOptimizationDemand ForecastingTime SeriesBayesian InferenceSimulationRegressionClassificationRandom ForestGenAIAgentic AIExplainabilityETL PipelinesAutomated ReportingEDAFeature EngineeringModel SelectionCross-ValidationDeploymentStreamlitReactPowerBITableauGitJupyterMatplotlibSeabornMySQLMongoDBPostgreSQLAWSGoogle Cloud PlatformGoogle ColabUnix/Linux

Work Experience

Data Science Intern

RentPrompts
Jan 2024Jun 2024

Evaluated 8+ GenAI and Agentic AI architectures for automated workflow use cases by benchmarking accuracy, latency, and cost trade-offs on Hugging Face and GPU-enabled environments. Launched 3 optimized models into the production platform by designing offline evaluation experiments measuring output quality and processing efficiency. Shortened experimentation cycles by prototyping a reusable data ingestion pipeline that standardized raw inputs into model-ready formats.

Data Analyst Intern

DisplayFort
Jan 2023Jun 2023

Increased operational conversion rate by 18% by designing a recommendation model with customer segmentation in Python and Scikit-Learn. Cut ad hoc data preparation time by 40% by building automated SQL pipelines. Improved quarterly demand projection accuracy by 15% through scenario analysis and simulation on 12 months of operational data.

Projects

Forecasting Optimization & Explainable Analytics (Capstone)

Outperformed naive volume forecasting baselines by 14.5% and 20.6% using an Empirical Bayesian–ARIMA ensemble with parameter optimization. Engineered a scalable data pipeline merging multi-source operational data into a unified analytical layer, applying feature engineering and dimensionality reduction.

PythonScikit-learnPyMCStreamlit

Predictive Classification & Model Selection (Federal Survey, 30K+ Records)

Achieved 88% accuracy across 30,000+ records by implementing and comparing multiple classification algorithms. Engineered 60+ features from noisy behavioral data and reduced prediction error by 21% through iterative model selection.

RRandom ForestXGBoost

Education

Master of Science in Data Science

Data Science

Seattle University2024 - 2026

Bachelor of Technology in Computer Science

Computer Science

Rajiv Gandhi Proudyogiki Vishwavidyalaya2019 - 2023
Prateek Pagare - Data Scientist | HiringAnt