Sr. Associate, Data Scientist

Revantage•Dallas, TX

1d•Hybrid

About The Position

The Data Scientist at Revantage will develop and deploy machine learning models to optimize revenue management and pricing strategies, analyze large datasets to generate actionable business insights, and collaborate cross-functionally to align data initiatives with company goals. This role directly impacts business operations for Blackstone Portfolio Companies by improving decision-making and operational efficiency through advanced analytics. It offers the chance to contribute to high-visibility projects that drive measurable value across the organization.

Requirements

5–7 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 time series forecasting and classification, optimizing business operations and decision-making.
Experience working in fast-paced environments with tight deadlines, managing multiple projects simultaneously.
Demonstrated ability to work effectively within a team, producing and reviewing code written by peers.
Bachelor’s degree in relevant field.
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.

Nice To Haves

Familiarity with revenue management theory and pricing strategies is a plus.
Advanced degree (master’s or Ph.D.) in a relevant field is a plus.

Responsibilities

Build and deploy machine learning models to optimize revenue management and pricing strategies.
Analyze large datasets to uncover insights and develop actionable recommendations for 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.