Andrew William Skrobola III

*** ******** **** ******* *****: aduk2c@r.postjobfree.com

Stone Mountain, GA 30087 C. 336-***-****

Key Qualifications:

• Experienced professional with versatile abilities in many programming languages. Ability to grasp and communicate analytic insights and implications through data visualization and interpersonal skills.

• Inspired by big challenges with a passion for constructive collaboration; Self- starter exhibiting strong understanding of computational and mathematic concepts of analytics algorithms. Education:

Georgia State University, Atlanta, GA, May 2022

Major: Masters of Business Administration/Masters of Data Analytics Clemson University, Clemson, SC, Spring 2016

Major: Marketing Minor: Legal Studies

Work Experience:

NICE Ltd. June 2022 – Current

Atlanta, GA

Data Science Intern-Text Analysis

• Built and tested speaker diarization algorithms in Python for analysis of customer engagement and interactions

• Experimented with prospective speaker activity detection techniques to improve overall diarization systems

• Analyzed and identified instances of overlapped speech in one-on-one customer audio data to identify areas of weakness improve employee engagement metrics

• Coordinated with cross-functional language teams to conceptualize and improve speech detection pipelines Projects:

Ryerson Metals Quote Prediction

• Analyzed 2 years of sales and external seeking relevant features for quote success prediction

• Designed, built, and tuned Machine Learning and Deep Learning models gaining over 80% accuracy Covid Healthcare Sentiment Analysis

• Scraped, cleaned, and processed 30,000 tweets related to relevant healthcare technology

• Performed time analysis of public sentiment change regarding healthcare tech advances from 2019-2022 Technical Skills:

• Programming: Python, MySQL, R

• Supervised Learning: Linear and Logistical Regressions, Decision Trees, SVM

• Unsupervised Learning: K-means clustering, Principal Component Analysis

• Natural Language Processing Models

• Speaker Diarization: Speaker Activity Detection, Overlapped Speech Detection, Speaker Segmentation

• Deep Learning: Neural Networks, Multi-Layer Perceptron, Hidden Layer Networks

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