Andrew Conti
Director Of Engineering at Postscript
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrew Conti is a seasoned engineering leader and founder with 13 years of experience building and scaling high-throughput, customer-facing platforms. He has led engineering at growth-stage companies and startups—architecting systems that supported 500M monthly visits at Reactive and 2.3M concurrent users at HQ Trivia—and drove Reactive from concept to a Postscript acquisition after raising $2M. Andrew combines hands-on backend expertise (Python/Django, scalable infra, data-driven systems) with strategic product leadership, launching features that turned into rapid ARR growth like shoppable ads. He’s experienced building and coaching distributed, high-autonomy remote teams and translating ambiguous product needs into pragmatic technical roadmaps. An early data practitioner, he also contributes machine-learning and API-focused tooling on GitHub, including a Django REST cookiecutter and a Kaggle Titanic tutorial, reflecting a blend of applied data science and production engineering. Based in New York, he thrives in fast-paced, product-first environments where autonomy and measurable business outcomes matter.
13 years of coding experience
12 years of employment as a software developer
Bachelor of Science Economics, Bachelor of Science Economics at College of CharlestonMasters Computational Finance, Masters Computational Finance at Columbia University
Chinese
Stackoverflow
17,941reputation
4.3mreached
271answers
78questions
error-handling
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Github Skills (41)
python10
django10
scikit10
pandas10
user-authentication10
machine-learning10
django-rest10
authentication10
api10
django-rest-framework10
scikit-learn10
rest-api10
logistic-regression10
random-forest10
data-analysis10
Programming languages (13)
Github contributions (5)
agconti/kaggle-titanic
May 2013 - Dec 2017
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 130 commits, 16 PRs in 4 years 8 months
Contributions summary:Andrew implemented machine learning models to predict passenger survival on the Titanic. The contributions focused on data cleaning, feature engineering, and model selection using algorithms like Logistic Regression and Random Forest. The user's work involved building a predictive model to estimate the probability of survival, analyze the results, and optimize the models for better performance in the Kaggle competition.
kaggletechniquespythonsuperviseddisaster
agconti/cookiecutter-django-rest
Jun 2015 - May 2021
Build best practiced apis fast with Python3
Role in this project:Back-end Developer
Back-end DeveloperContributions:8 releases, 500 commits, 890 PRs in 6 years 1 month
Contributions summary:Andrew primarily contributed to the creation of user-related features within the Django REST framework. They developed a basic user model, including migrations and admin configurations. Additionally, they added views, serializers, and permissions to support API functionality. Furthermore, they set up authentication using tokens. This involved creating and modifying several files related to user management and API integration.
apipythonboilerplateasynciodjango
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