Data Analyst

Rafael Nunez

San Francisco, CA ***** 562-***-**** **********@*****.***

Highly analytical and detail-oriented Data Analyst with a strong background in astronomical data processing and telescope operations. Experienced in developing data pipelines, performing spectral analysis, and generating data-driven insights using Python, SQL, and machine learning techniques, with a proven ability to communicate complex data findings to diverse audiences.

Education

Master of Science in Astronomy & Astrophysics San Francisco State University

AUG 2022 - JAN 2025 Cumulative GPA: 3.84

Bachelor of Science in Astronomy & Astrophysics University of California, Santa Cruz

SEP 2017 - JUN 2021 Cumulative GPA: 3.40

Experience

Observer & Researcher Spectroscopic Analysis of Luminous Variables and Transients in Our Neighbor (SALVATION) Project PI: Raja Guha Thakurta (UCSC)

OCT 2019 - PRESENT

Observed variable stars and transients using the Shane 3m Telescope at Lick Observatory.

Developed template-matching pipelines for stellar spectra classification.

Analyzed multi-epoch spectral and photometric data, integrating SQL for dataset management.

Applied deep neural networks for atmospheric parameter predictions.

Collaborated on outreach presentations to communicate research to non-expert audiences.

Research Intern NSF NOIRLab Tucson, AZ (Remote)

JUN 2023 - AUG 2023

Conducted spectral line analysis to classify transient spectroscopic data from Lick/KAST and Gemini/GMOS.

Researched machine-learning algorithms to predict stellar parameters and generated synthetic template libraries.

Utilized Python and SQL for data extraction, transformation, and statistical analysis of large astrophysical datasets.

Prepared manuscript for publishing analysis results.

Graduate Teaching Assistant San Francisco State University, Astronomy & Astrophysics Department

AUG 2022 – DEC 2023

Analyzed student performance and assessment data to identify trends and inform targeted instructional strategies.

Collaborated with faculty to improve data-driven learning outcomes in a post-pandemic educational environment.

Developed and implemented interactive Python-based exercises to visualize and teach quantitative concepts.

Applied statistical analysis to evaluate student engagement metrics and optimize teaching methods.

Skills

Data Analysis & Statistical Modeling: Regression analysis, hypothesis testing, predictive modeling

Programming & Scripting: Python (NumPy, Pandas, Scikit-learn, Matplotlib - Advanced proficiency), SQL, HTML, CSS, Java

Microsoft Office Suite (Excel, Word, PowerPoint)

Data Visualization: Matplotlib, Seaborn

Scientific Communication & Outreach

Database Management: SQL queries, data extraction, cleaning, and transformation

Time Series Analysis & Forecasting

Telescope Operation & Spectral Analysis

Machine Learning: Deep Learning, Synthetic Template Libraries

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