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 groups.
- Passion for continuous learning and innovation in machine learning and cloud technologies.
