Data Scientist

Total Wine & More

About The Position

Total Wine & More is seeking a Data Scientist to join our growing Data Services team in our Bethesda, MD office. You will play a pivotal role in designing, developing, and deploying machine learning and AI solutions that drive strategic decision-making and operational efficiency across Total Wine & More business. You will be responsible for supporting the full lifecycle of machine learning and AI development—from initial ideation and business problem framing through model development, deployment, and ongoing performance monitoring. This role requires a strong foundation in data science, with a deep interest in learning about production-grade ML systems, and a proactive approach to translating business needs into technical solutions. This role reports to the Senior Director, Data Science.

Requirements

Bachelor's Degree in Data Science, Computer Science, Mathematics, Statistics, Economics or related fields required or equivalent years of experience
1-4 years of experience in data science, predictive analytics, econometrics or related fields preferred
Hands-on business experience designing machine learning and AI models by framing business problems, engineering features, and selecting appropriate algorithms and architectures.
Experience in training models through data preparation, algorithm fitting, hyperparameter tuning, and cross-validation to ensure robustness and generalizability.
Hands-on business experience in validating model performance using statistical metrics, conducting fairness and bias assessments, and performing error analysis to improve model quality.
Hands-on business experience in deploying models into production environments, including packaging, system integration, automation of deployment workflows, and maintaining clear documentation.
Hands-on business experience in monitoring deployed models by tracking performance, detecting data drift, triggering retraining, and implementing logging and alerting mechanisms.
Proficiency in Python for data analysis, modeling, and automation, along with strong SQL skills for data extraction, transformation, and querying across large-scale databases

Nice To Haves

Preferred hands-on experience with cloud platforms (e.g., AWS, Azure, GCP) and automation tools(Airflow, Argo, etc.) for scalable model development, deployment, and monitoring, including use of cloud-native tools and services for data science workflows.

Responsibilities

designing machine learning and AI models by framing business problems, engineering features, and selecting appropriate algorithms and architectures.
Train models by preparing data, fitting algorithms, tuning hyperparameters, and validating robustness through cross-validation techniques.
Validate model performance using statistical metrics, conduct fairness and bias assessments, and perform error analysis to refine model quality.
Deploy models into production environments by packaging them appropriately, integrating with systems, automating deployment workflows, and documenting for maintainability.
Monitor deployed models by tracking performance over time, detecting data drift, triggering retraining when necessary, and implementing logging and alerting mechanisms.
Communicate model results and trade-offs to leadership and stakeholders.