What are the responsibilities and job description for the Machine Learning Data Engineer position at BNSF Railway?
BNSF Railway's Network Strategy and Innovation team develops strategic initiatives and innovative solutions to enhance network efficiency and sustainability. This team optimizes railway infrastructure and operations by analyzing market trends, customer needs, and technological advancements. Their work includes adopting advanced technologies, improving operational efficiency, and exploring sustainable practices.
We are seeking a Machine Learning Data Engineer to play a critical role in building and maintaining the data infrastructure and pipelines necessary to support BNSF's Network Strategy, Design, and Innovation department. This position involves leveraging machine learning techniques to enhance data-driven decision-making and optimize operations within rail systems.
Responsibilities
- Designing, developing, and deploying scalable data pipelines and machine learning models.
- integrating machine learning models into production environments, ensuring reliability and scalability
- Build and maintain robust data pipelines for ingesting, transforming, and storing large datasets.
- Develop backend infrastructure to support machine learning models and AI solutions.
- Implement and automate machine learning model pipelines, ensuring seamless deployment and monitoring.
- Collaborate with data scientists to optimize models for performance and scalability.
Required Skills
- Strong proficiency in Python and SQL, with experience in building data pipelines and deploying machine learning models.
- Advanced analytical skills and the ability to independently conduct complex analyses.
- Excellent interpersonal and communication skills, both verbal and written.
- Ability to manage multiple projects and deadlines simultaneously.
- Proficiency in using data visualization tools such as Tableau or Power BI.
- Experience with machine learning frameworks and tools (e.g., TensorFlow, PyTorch).
Preferred Qualifications
- Bachelor’s and/or Master's degree in analytics, computer science, or a related field.
- 3 years of experience in ML/Optimization
- Demonstrated experience with machine learning life cycle management and operationalization.
- Knowledge of deep learning model building and implementation.
- Experience with cloud platforms (AWS, Azure, GCP) and CI/CD tools (e.g., Jenkins, GitLab CI).
- Familiarity with PySpark and experience working with Databricks.
Salary : $78 - $83
