Jayanti Gautam

Data Analyst | Machine Learning Engineer | Python Developer

Talent

LucknowMember since 18 May 2026

sqlpower biadvanced exceldata visualizationmldata analystEDAFeature EngineeringData CleaningPythonPandasNumPyScikit-LearnC++TensorFlowPyTorchYOLOv5NLPObject DetectionKaggleArcGISQGIS

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sqlpower biadvanced exceldata visualizationml

About

Results-driven Data Analyst with strong experience in data wrangling, exploratory analysis, and visualization using Python (Pandas, NumPy), SQL, and Power BI. Proven ability to extract actionable insights from complex datasets, optimize business processes, and communicate findings to stakeholders. Skilled in machine learning and predictive modeling with a track record of delivering measurable outcomes: 40% reduction in manual review, 20% faster inference, 90%+ detection accuracy.

Adept at building end-to-end data pipelines and collaborating with cross-functional teams.

Experience

Data Analytics Intern — Object Detection & Deep Learning - EI Systems - Remote

(2024-06 - 2024-07)

Preprocessed 10,000+ records using NumPy & Pandas; performed data cleaning, feature engineering, and exploratory analysis to prepare production-ready datasets.
Developed and optimized classification models (Linear Regression, Decision Tree, SVM) with Scikit-Learn; achieved 85% accuracy and 90%+ prediction stability.
Improved YOLOv5 + OpenCV real-time detection pipeline via transfer learning, cutting inference time by 20% and boosting accuracy by 12%.

Data Analytics Research Intern — AI for Smart Agriculture - Suvidha Mahila Mandal - Remote

(2025-09 - 2025-11)

Designed NLP pipelines to auto-summarize 500+ crop disease reports, reducing manual review effort by 40% and improving analyst throughput.
Built an intelligent farmer query-answering system (NLP) and crop price forecasting model achieving 75%+ validation accuracy on real agricultural datasets.

Data Analytics Intern — Telecom Customer Churn Prediction - Saiket Systems - Remote

(2025-11 - 2025-12)

Built supervised classification model on telecom demographic, service, and billing data; achieved 80% accuracy with significantly improved minority-class recall.
Executed end-to-end EDA, feature engineering, and SMOTE-based class balancing; benchmarked Logistic Regression, Decision Tree, and Random Forest for best-fit selection.