dhaneshbb

Data Scientist

https://www.opentalent.in/dhaneshbb

Bengaluru, Karnataka, IndiaMember since June 16, 2026

Key Strengths

Strong foundational understanding of machine learning concepts, including regression (Lasso, XGBoost) and classification (XGBoost).
Demonstrated ability to handle complex data challenges such as multicollinearity, class imbalance, data leakage, and missing data.
Proficiency in Python for data analysis, machine learning, and scripting.
Experience with feature engineering, statistical analysis, and model evaluation metrics (R2, ROC AUC, recall, accuracy).
Practical application of data science techniques in diverse projects like price prediction, sales lead quality, and loan default prediction.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's portfolio showcases a strong passion for data science and machine learning through numerous personal projects. The diversity of projects, from data analysis tools to complex predictive models, indicates a broad interest and willingness to explore different problem domains. The focus on personal projects, while demonstrating initiative, means there is no direct evidence of experience in team environments or corporate culture. The target role of 'Data Scientist' aligns well with the technical skills demonstrated in the projects.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a proactive and self-directed approach to learning and problem-solving. The creation of tools like 'autocsv-profiler-suite' and 'insightfulpy' suggests an inclination towards developing reusable solutions and improving workflows. However, without formal work experience or psychometric test results, it is difficult to assess collaboration, stress handling, or direct operational fit.

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Projects

dhaneshbb

February 6, 2026 – Present

GitHub profile README — Data science, ML, economics and portfolio links.

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autocsv-profiler

September 25, 2025 – Present

Python toolkit for automated CSV data analysis. Features interactive CLI, automatic delimiter detection, memory-efficient processing, statistical profiling, and visualization generation. Supports Python 3.8-3.13. Perfect for analysts working with CSV files.

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Multi-AI-Chat-Manager-demo

September 10, 2025 – December 11, 2025

Desktop application for managing multiple AI chat services simultaneously with automated window control and synchronized prompt distribution. Features unified AI window management, grid layouts, and YAML configuration. Documentation-only demo with Electron frontend and Python backend concepts for Windows 10/11.

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spectratact

September 10, 2025 – October 7, 2025

Desktop application management tool with intelligent window orchestration. Manage and arrange multiple applications across monitors with customizable grid and side-by-side layouts. Windows 10/11.

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dhaneshbb.github.io

July 4, 2025 – Present

Static portfolio website using vanilla JavaScript ES6 modules and Bootstrap 5. Component-based architecture with dynamic nav/footer loading. Modular CSS organization, Intersection Observer for animations. ESLint, Stylelint, Prettier for code quality. GitHub Actions Lighthouse CI workflow. Deployed on GitHub Pages.

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autocsv-profiler-suite

April 8, 2025 – October 9, 2025

Multi-environment CSV data analysis orchestrator that resolves dependency conflicts between profiling engines through isolated conda environments while providing a unified interface.

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FicZon-Sales-Effectiveness

March 10, 2025 – November 10, 2025

B2B sales lead quality prediction using XGBoost classifier. Achieves 81.06% ROC AUC and 84.74% recall on 7,420 IT sales leads. Handles class imbalance, high-cardinality categoricals, and missing data through frequency encoding and threshold optimization. Includes statistical analysis, cross-validation, feature importance, and business insights.

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HomeLoanDef

February 28, 2025 – November 9, 2025

Home loan default prediction analyzing 58.44M records across 7 datasets. XGBoost classifier with 143 engineered features achieves 83% accuracy, 52.8% recall, and 0.785 ROC AUC at threshold 0.60. Addresses class imbalance (8% default rate), memory optimization (68.5% reduction), and multicollinearity.

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AutoPricePred

February 28, 2025 – November 9, 2025

Machine learning regression model predicting 1985 automobile prices. Lasso model achieves 91.7% R2 with superior generalization over XGBoost. Handles extreme multicollinearity (VIF 16,676→8.36), data leakage detection, and outlier treatment through PCA and domain-driven feature engineering.

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insightfulpy

February 6, 2025 – Present

Python package for exploratory data analysis providing statistical summaries, data quality checks, outlier detection and batch visualization functions. Supports Jupyter notebooks and terminal environments.

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Key Strengths

Strong foundational understanding of machine learning concepts, including regression (Lasso, XGBoost) and classification (XGBoost).
Demonstrated ability to handle complex data challenges such as multicollinearity, class imbalance, data leakage, and missing data.
Proficiency in Python for data analysis, machine learning, and scripting.
Experience with feature engineering, statistical analysis, and model evaluation metrics (R2, ROC AUC, recall, accuracy).
Practical application of data science techniques in diverse projects like price prediction, sales lead quality, and loan default prediction.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

dhaneshbb

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit