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# Sr. Associate, Data Scientist

- **Company:** Revantage Corporate Services
- **Location:** Chicago, IL, United States
- **Experience:** Expert
- **Salary:** $123,684.0 - $164,868.0
- **Contract:** Permanent contract
- **Skills:** Microsoft Azure, Big Data, Data Mining, Python (Programming Language), Machine Learning, Software Engineering, SQL Databases, Gitlab, Build Management, Scikit Learn, Information Technology, Statistics Packages, Software Version Control
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f62e721d22e5f9ed

## About the Role

* 3-6 years of professional experience in data science, machine learning, software engineering, or related fields, leveraging statistics and programming to drive business decisions.
 * Proven expertise in building machine learning models, including predictive and forecasting models, optimizing business operations and decision-making.
 * Experience working in fast-paced environments while producing rigorous, well-documented work.
 * Demonstrated ability to work effectively within a team, producing and reviewing code written by peers.
 * Bachelor's degree in relevant field, e.g., statistics, computer science, data science, etc.

Technical Skills:

 * Advanced proficiency in Python, including libraries such as scikit-learn and statsmodels.
 * Strong expertise in SQL for data extraction and transformation.
 * Familiarity with GitLab/Azure Devops for collaborative development and version control.
 * Strong understanding of evaluation metrics and hyperparameter tuning for machine learning models.
 * Solid grounding in statistics for inference and analysis.
 * Familiarity with time series analysis, regression models, and econometric techniques.

Preferred:

 * Familiarity with revenue management theory and pricing strategies is a plus.
 * Experience in real estate finance or operations is a plus.
 * Advanced degree (master's or Ph.D.) in a relevant field is a plus.

## Description

* Build and deploy machine learning models to optimize revenue management and pricing strategies.
 * Analyze large datasets to develop models that can drive more informed and consistent business decision-making.
 * Collaborate with peers outside the data science team to align data science initiatives with business goals.
 * Continuously evaluate and improve model performance through testing, tuning, and validation.
 * Document processes, methodologies, and findings for both technical and non-technical audiences.


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