Bart Amin

Data Scientist

https://www.opentalent.in/bart-amin

Member since June 24, 2026

Key Strengths

Demonstrated experience in advanced RAG (Retrieval Augmented Generation) techniques, including fine-tuning retrieval heads and hierarchical clustering for context curation.
Proficiency in various machine learning paradigms, specifically clustering algorithms (K-Means, AHC, GMM, OPTICS, HDBSCAN) and time series analysis (VAR models).
Strong project-based learning and implementation skills, evidenced by multiple personal projects in relevant areas.
Experience with Python and R, key languages for data science and machine learning.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects demonstrate a strong interest in cutting-edge AI/ML research and practical application, which aligns well with an innovative data science culture. However, the lack of professional experience or team-based projects makes it difficult to fully assess cultural fit beyond technical curiosity.

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

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Projects

Fine-tuned-RAG

May 19, 2026 – Present

I developed a fine-tuned retrieval head for RAG that learns to more reliably retrieve relevant passages by transforming the query embeddings before retrieval. It is trained on synthetically generated question-chunk pairs from the corpus, and benchmarked against standard top-K cosine similarity on the isaacus/legal-rag-bench legal corpus.

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Clustered-Dynamic-RAG

April 12, 2026 – Present

CDRAG is a new retrieval framework that uses hierarchical document clustering, and LLM-guided document selection from those clusters to curate the context most efficiently. It is benchmarked against standard top-K RAG on 100 legal questions from the Legal RAG Bench dataset.

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Time-Varying-VAR-bootstrapping

March 24, 2025 – July 8, 2025

Benchmarks Time-Varying VAR models (TV-VAR GAM and LTV-VAR) on their ability to detect non-stationarity in multivariate time series, with ROC and Type I error analysis.

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Clustering-Algorithms

March 12, 2025 – March 12, 2025

Compares centroid, hierarchical, distribution, and density-based clustering algorithms (K-Means, AHC, GMM, OPTICS, HDBSCAN) across multiple datasets with visualization and cluster quality metrics in R.

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Key Strengths

Demonstrated experience in advanced RAG (Retrieval Augmented Generation) techniques, including fine-tuning retrieval heads and hierarchical clustering for context curation.
Proficiency in various machine learning paradigms, specifically clustering algorithms (K-Means, AHC, GMM, OPTICS, HDBSCAN) and time series analysis (VAR models).
Strong project-based learning and implementation skills, evidenced by multiple personal projects in relevant areas.
Experience with Python and R, key languages for data science and machine learning.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.

Bart Amin

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit