Total Wine & More · 1 week ago

Lead Machine Learning Engineer

Bethesda, MD

Full-time

Onsite

Senior Level

6+ years exp

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Hiring Manager

Mel Brown

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Responsibilities

Manage and deploy machine learning models developed by Data Science team into production – integrated with the team through the development process.

Support the retraining, monitoring, and measurement of deployed machine learning models – covering a wide spectrum of ML approaches.

Create common frameworks for delivering modeling results to systems within TWM.

Build out tools to support data scientists – lowering the level of effort to create and prototype machine learning models, such as building a feature data store.

In collaboration with the Data Platform team, make sure ML results are published within TWM data warehouse and easily digestible by end users.

In collaboration with Engineering and Architecture teams, build and maintain MLOps tools and frameworks – utilizing DevOps tools currently in place at Total Wine & More.

In partnership with Data Science team members, maintain documentation and manage the transition of production processes to production support team, when applicable.

Qualification

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MLOpsData cleaningPreprocessingStorageBatch pipelinesStreaming pipelinesArgoAirflowMachine learning modelingScikit-learnPandasNumpyXgboostPytorchPython programmingClean code writingTechnical presentationA/B testingProblem-solvingCommunicationTeamworkMachine LearningRayKubeFlowWeights & BiasesRetail Experience

Required

Bachelor's degree from four-year College or university in Computer Science, Mathematics, Statistics or related field. Master’s or PhD preferred.

6+ years of experience in data science, computer science or related fields or 5+ years with Master’s degree.

6+ years of experience in software engineering, data engineering, or related fields or 5+ years with Master’s degree.

Strong understanding of MLOps principles and practices with experience of supporting a data science team through the ML lifecycle to include deployment and monitoring.

Experience with operationalizing, monitoring, and scaling machine learning models and pipelines in cloud ecosystems.

Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage. Deep understanding of batch and streaming pipelines as well as orchestrators like Argo and Airflow.

Fundamental understanding of machine learning modeling techniques and algorithms. Experience using machine learning frameworks such as scikit-learn, pandas, numpy, xgboost, and pytorch.

Solid programming skills in Python and the ability to write clean, efficient, and well-documented code.

The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds.

The ability to present technical concepts and results in an audience-appropriate way

Understanding of A/B testing