Results-driven, analytically strong **Data Scientist and Machine Learning Engineer** with hands-on experience building, optimizing, and applying machine learning models for complex, data-constrained problem domains. Skilled in statistical analysis, **A/B testing**, **predictive modeling,** and **AI-driven automation, with a strong ability to convert complex data into actionable insights**. Worked as an **intern at Channel Science** on a high-impact U.S. nuclear monitoring–aligned project focused on **classifying earthquakes versus nuclear explosions** using seismic wave patterns (P-waves vs. S-waves). Contributed to **decoding and digitizing legacy seismographic data** stored on magnetic tapes, necessitated by the Nuclear-Test-Ban Treaty restrictions on new data collection. Applied **sequence-to-sequence models for seismic signal decoding** and **autoencoder-based anomaly detection** to distinguish natural seismic events from explosion signatures. The work was supported by an SBIR Grant (DE-SC0021879) and was recognized with a **LinkedIn recommendation from the Technology Leader**. Won Hackathons, published papers, and Won for presenting a paper in a conference involving **Machine Learning, Generative AI,** and **Agentic AI** to drive innovation and strategic growth.
Results-driven, analytically strong **Data Scientist and Machine Learning Engineer** with hands-on experience building, optimizing, and applying machine learning models for complex, data-constrained problem domains. Skilled in statistical analysis, **A/B testing**, **predictive modeling,** and **AI-driven automation, with a strong ability to convert complex data into actionable insights**. Worked as an **intern at Channel Science** on a high-impact U.S. nuclear monitoring–aligned project focused on **classifying earthquakes versus nuclear explosions** using seismic wave patterns (P-waves vs. S-waves). Contributed to **decoding and digitizing legacy seismographic data** stored on magnetic tapes, necessitated by the Nuclear-Test-Ban Treaty restrictions on new data collection. Applied **sequence-to-sequence models for seismic signal decoding** and **autoencoder-based anomaly detection** to distinguish natural seismic events from explosion signatures. The work was supported by an SBIR Grant (DE-SC0021879) and was recognized with a **LinkedIn recommendation from the Technology Leader**. Won Hackathons, published papers, and Won for presenting a paper in a conference involving **Machine Learning, Generative AI,** and **Agentic AI** to drive innovation and strategic growth.