Lead, Data Science

Total Wine & More

About The Position

Total Wine & More is seeking a Lead, Data Scientist to join our Technology team in Bethesda, Maryland. As a Lead Data Scientist, 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. You will be expected to act independently to deliver high-impact technical solutions, taking ownership of projects from concept to execution. You will mentor junior team members on technical trade-offs on solutions and provide thought leadership about how different problems can be solved. You will report to the Sr. Director, Data Science.

Requirements

Bachelor's Degree in Data Science, Computer Science, Statistics, Mathematics, Economics or related discipline required or equivalent years of experience
5-8 years in data science, predictive analytics, econometrics, software engineering, data engineering or related fields preferred
Proven expertise in designing and architecting advanced machine learning and AI solutions, including leading efforts to frame complex business problems, define scalable feature engineering strategies, and select optimal algorithms and architectures for enterprise-level applications
Proven expertise in model training and optimization, with the ability to design efficient training pipelines, implement distributed training strategies, and apply sophisticated hyperparameter tuning techniques to maximize performance and scalability
Proven expertise in model validation and governance, including establishing rigorous evaluation frameworks, conducting comprehensive fairness and bias audits, and driving continuous improvement through advanced error analysis and benchmarking
Proven expertise in production deployment of ML systems, including designing robust CI/CD pipelines, implementing containerization and orchestration (e.g., Docker, Kubernetes), and ensuring compliance with security and reliability standards across cloud environments
Oversight of model monitoring and lifecycle management, including building automated monitoring systems, implementing drift detection and retraining workflows, and defining alerting mechanisms to maintain long-term model health and business impact
Expert-level programming skills in Python and SQL, with the ability to develop production-grade code, optimize queries for large-scale datasets, and mentor team members on best practices for coding and data management

Nice To Haves

Master's Degree in Data Science, Computer Science, Statistics, Mathematics, Economics or related discipline preferred

Responsibilities

Design and develop machine learning and AI solutions by translating complex business problems into modeling approaches, engineering features, and selecting appropriate algorithms and architectures.
Build scalable, reusable solutions adaptable across multiple business use cases.
Lead work on large, multi-dimensional business problems, owning end-to-end modeling strategy and execution.
Train and optimize models through data preparation, algorithm selection, hyperparameter tuning, and cross-validation, clearly articulating trade-offs between modeling approaches and their business implications.
Evaluate and refine models using statistical performance metrics, fairness and bias assessments, and error analysis to ensure robust, responsible outcomes.
Define evaluation metrics that align directly to business outcomes and communicate how model performance improvements translate to measurable business value.
Deploy models to production by packaging solutions, integrating with downstream systems, automating deployment workflows, implementing error handling, and maintaining clear documentation.
Monitor models in production by tracking performance, detecting data and concept drift, triggering retraining, and implementing logging and alerting mechanisms.
Provide technical mentorship and thought leadership by guiding junior team members through trade-offs, solution design, and effective application of machine learning to business problems.
Communicate model insights, limitations, and trade-offs clearly to senior leadership and cross-functional stakeholders.