7-Eleven is an iconic family of brands with over 86,000 locations, surpassing every retailer in the world. We revolutionize convenience, restaurants and fuel through cutting edge innovation — working hard to be the customer's first choice. 7-Eleven empowers our employees to "activate awesome" and make a meaningful impact in their stores and communities every day. If you're ready to grow, lead and make a difference, come join our team and help shape the future of convenience.
Job Summary
We are seeking a talented Machine Learning Engineer to design, develop, and deploy scalable machine learning solutions that drive business value as part of our digital initiatives. As a Machine Learning Engineer II at 7-Eleven, you will collaborate with cross-functional teams and work closely with senior architects to implement scalable and efficient data solutions. Your expertise in big data technologies, cloud-based data platforms, and machine learning model development, will contribute to the transformation of telemetry data and other large datasets into actionable insights.
Key Responsibilities:
Design, develop, test, and deploy machine learning models and data-driven solutions that transform store equipment telemetry data into actionable insights.
Work in enterprise environments to build and optimize scalable data pipelines using Python, PySpark, and Azure-based technologies.
Process, analyze, and manipulate large structured and unstructured datasets to support analytics and machine learning initiatives.
Collaborate with data engineers, data scientists, product owners, and business stakeholders to translate business requirements into technical solutions.
Develop, evaluate, and tune machine learning models using appropriate algorithms and statistical techniques.
Implement MLOps best practices for model deployment, monitoring, and lifecycle management.
Create visualizations, dashboards, and presentations to communicate insights and recommendations to technical and non-technical audiences.
Participate in code reviews, technical design discussions, and continuous improvement initiatives.
Required Qualifications:
Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field.
5+ years of experience delivering big data and machine learning solutions in enterprise environments.
Strong programming experience in Python and PySpark.
Hands-on experience with Azure Databricks, Azure Data Factory (ADF), Azure Kubernetes Service (AKS), Git, and Azure DevOps.
Experience building, training, validating, and deploying machine learning models.
Solid understanding of data structures, algorithms, software engineering principles, and distributed computing concepts.
Experience working with large-scale datasets and cloud-native architectures.
Strong analytical, problem-solving, and communication skills.
Preferred Qualifications:
Experience deploying and operationalizing machine learning models in Azure Databricks.
Experience with MLOps frameworks and CI/CD pipelines for machine learning workloads.
Experience with data visualization tools such as Power BI, Tableau, or equivalent platforms.
Experience working with equipment telemetry data or on equipment maintenance projects.
Knowledge of containerization technologies and cloud-native application development.
Machine Learning Expertise:
Clustering and segmentation techniques.
Generalized Linear Models (GLM), Linear Regression, and Logistic Regression.
Decision Trees and Random Forests.
Gradient boosting techniques including XGBoost.
K-Nearest Neighbors (KNN).
Support Vector Machines (SVM).
Artificial Neural Networks (ANN) and deep learning concepts.
Model evaluation, feature engineering, hyperparameter tuning, and performance optimization.
Success Factors:
Ability to work effectively in a fast-paced, collaborative environment.
Strong ownership mindset and commitment to delivering high-quality solutions.
Ability to communicate complex technical concepts to diverse stakeholder…