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# Data Scientist

- **Company:** POST CODE, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $56,400.0
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Amazon Web Services, Data Analysis, Continuous Integration, Python (Programming Language), Machine Learning, Azure Machine Learning, SQL Databases, Feature Engineering, Snowflake, Gitlab, Pandas, Scikit Learn, Statistics Packages, Machine Learning Operations, Docker
- **Published:** October 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pthoy13h9h

## About the Role

* At least 4-6 years of experience as a Data Scientist, with a proven track record of taking models from an idea to a live, production system.
 * You have a strong, practical foundation in statistical and machine learning techniques, especially for predictive modeling, regression and classification techniques, and understanding causal impact and A/B testing.
 * You're fluent in Python (Pandas, Scikit-learn, Statsmodels etc.) and write strong, clean SQL (we use Snowflake).
 * You believe in MLOps and building things that last. You're comfortable with concepts like containerizing models with Docker and CI/CD, and have hands-on experience with GitLab/AWS SageMaker.
 * You're more than just a modeler-you're a great communicator and a true team player who enjoy collaborating and can guide stakeholders, manage expectations, and make the complex simple.

## Description

* You'll apply your skills in predictive modeling and advanced statistical techniques (like regression and classification techniques, forecasting, A/B testing and causal inference), to deeply understand and predict customer behavior, helping us make smarter decisions about retention and value.
 * We mean it. You will take ownership of your models from end-to-end: from data exploration in Snowflake and feature engineering to deploying your models on AWS SageMaker.
 * Be responsible for making sure your work is automated, tested, and reliable. You will work closely with GitLab.
 * You'll be the one translating fuzzy business questions into concrete data science problems and, just as importantly, translating your complex results back into clear, actionable insights for your stakeholders.
 * You'll work closely with our Machine Learning Engineering and Platform team to continuously improve our shared tools, ML platform and best practices.