GEOVANI A. MONTOYA

909-***-**** adcmrn@r.postjobfree.com

MEDIA

LinkedIn: geovani-montoya

Github: geovani-montoya

Kaggle: geokywankenobi

Portfolio: geovani-montoya.github.io

EDUCATION

DEC 2021

MS in Computational Finance &

Risk Management

UNIVERSITY OF WASHINGTON, SEATTLE

DEC 2019 JUL 2020

MS in Physics MS in Chemistry

CALIFORNIA STATE UNIVERSITY, LA

SEPT 2017-DEC 2018

Non-Degree in Applied and

Computational Mathematics

CALIFORNIA INSTITUTE OF TECHNOLOGY

JUN 2013

BS in Chemistry (ACS certification)

UNIVERSITY OF CALIFORNIA, IRVINE

PROFESSIONAL DEVELOPMENT

JAN 2019-APR 2020

Nanodegrees in Data Scientist,

Artificial Intelligence for Trading,

Natural Language Processing,

Data Engineer

UDACITY

DEC 2019-APR 2020

Big Data and Data Science Master

INTELLIPAAT

PROFESSIONAL AREAS

§ Machine Learning

§ Data Engineering and Big Data

§ Data Visualization

§ Statistics and Probability

§ Natural Language Processing

§ Time-Series

§ Credit Risk Analysis

§ Customer Service Analytics

TECHNICAL SKILLS

§ Python, R, SQL, BASH

§ Hadoop, Kafka, Hive, Cassandra,

HBase, Redshift, Airflow

§ Tensorflow, Keras, PyTorch,

Spark, Scikit-learn

§ Tableau, Power BI

§ AWS

DATA SCIENCE PROJECTS

Artificial Intelligence for Quantitative Research OCT-DEC 2019

§ Developed NLP models to text mine corporate filings, such as 10Q and 10k statements, using bag-of-words and TF-IDF to generate company-specific sentiments and signal optimal times to buy/sell their stock.

§ Built deep neural networks to process and interpret news data by constructing and training LSTM networks for sentiment classification.

§ Developed deep learning models to generate trading signals using RNN and LSTM algorithms and evaluated results via backtesting. Customer Segmentation APR-MAY 2019

§ Built and optimized machine learning models (PCA and K-means) to predict target customers for mail-in advertisements using Scikit-learn libraries in Python.

§ Developed data pipeline for the data wrangling and feature engineering process to optimize machine learning models real-time. L.A.’s Homeless Population Predictions with Data Kind JUL 2018

§ Developed advanced statistical models, such as survival analysis, that captured the missing dynamics in previous models, used by legislation, to estimate annualized population experiencing homelessness; published in Los Angeles Times and Economic Roundtable.

EXPERIENCE

Financial Services Professional New York Life APR 2019-PRESENT

§ Top performer for three consecutive months based on increasing revenue by

$40,000.

§ Created/optimized investment and insurance portfolios for clients.

§ Designed/implemented marketing plans, including outreach programs and community events, resulting in an increase of monthly sales by 25%. Research Fellow CSULA JUL 2013–NOV 2018

Visiting Researcher Comp. Env. Science Lab at UC Irvine JUN-AUG 2014

§ Developed many-body theoretical models for semi-conductors with code (for numerical analysis) accurately predicting experiments.

§ Served as liaison/lead scientist between 3 collaborating teams.

§ Built data pipeline to automated data analysis via scripting in Python, increasing speed of analysis from 12 hrs/dataset to less than 5 min/dataset

(total of 500+ datasets).

Research Intern UC San Diego JUN-AUG 2015

§ Used high-performance computing to run molecular dynamic simulations via object-orient programming in Fortran and Python and calculated probability distributions of chemicals from simulation data.

§ Developed a pipeline in BASH to clean and manage large data sets. Data Analyst Researcher Atmospheric Integrated Research Institute at UC Irvine JAN 2012-JUN 2013

§ Automated data analysis and performed QQ tests using MATLAB to identify an quantify molecular concentrations of air particles.

§ Used linear regression to quantify concentrations and develop metrics used to report on grants and future publications.

ACTIVITIES

Dean of Pasadena Chapter School of Artificial Intel. AUG 2018-NOV 2019 Active Competitor (highest rank was top-6%) Kaggle

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