Zachariah J.

Software Engineer

San Francisco, California, United States1 yr 11 mos experience

Key Highlights

Trained ML models achieving high accuracy.
Developed impactful web applications improving team efficiency.
Identified disease risks using advanced bioinformatics techniques.

Stackforce AI infers this person is a Bioinformatics and Software Engineering professional with a focus on SaaS solutions.

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Skills

Core Skills

Web DevelopmentSoftware Engineering PracticesMachine LearningData AnalysisDatabase ManagementBioinformatics

Other Skills

HTMLJavaScriptLSTMsscikit-learntensorflowGitHub Issues APICSSNode.jsSQLRPython (Programming Language)LinuxPythonRStudioComputer Vision

About

I'm a Software Engineer with an entrepreneurial sprit, passionate about solving real world problems and learning about all things Computer Science. While my ML models are training and I am away from my computer I love to lift weights, play basketball, and watch football.

Experience

1 yr 11 mos

Total Experience

11 mos

Average Tenure

Current Experience

Uber

Software Engineer

Sep 2024 – Apr 2026 · 1 yr 7 mos · San Francisco Bay Area · Hybrid

Uber Eats - Logistics Predictions

Geico

Software Engineer

May 2024 – Sep 2024 · 4 mos

Github

Software Engineer Intern

Jun 2022 – Jun 2023 · 1 yr · Remote

Trained a Helpdesk Ticket Classifier with 93% accuracy, leveraging LSTMs, scikit-learn and tensorflow, facilitating real-time ticket labeling and analytics of stored ticket data for the IT department
Developed a real-time, full-stack GitHub Issues web app with Github Issues API, HTML/CSS/JS and Node.js, uncovering 20 long-standing issues and improving my team’s issue turnaround
Implemented a full-stack database web app, using HTML/CSS/JS and SQL, streamlining data manipulation for my team’s 5 most frequently used tables

HTMLJavaScriptWeb DevelopmentSoftware Engineering Practices

Tgen - part of city of hope

2 roles

Bioinformatics Intern/Helios Scholar

Jun 2021 – Aug 2021 · 2 mos · Phoenix, Arizona, United States · Hybrid

Identified individuals’ predisposition for 32 diseases given their polygenic risk score relative to 500k individuals, using a Linux Cloud Supercomputer, Python, and R, revealing 95% of the population is at risk of 1 or more diseases
Determined 5% of the population contains risk scores considered to be very high risk, highlighting benefits of early medical intervention before symptoms manifest

RPython (Programming Language)BioinformaticsData Analysis

Bioinformatics Researcher

Jan 2020 – Apr 2020 · 3 mos · Phoenix, Arizona, United States · Hybrid

Programmed an RNA scanner in R that detects clusters of genes to be over/underexpressed regions causing cancer, providing a list of 177 genes in our test patient that can be targeted by pharmaceuticals.

RRStudioBioinformaticsData Analysis