Jobs for People with MS: National MS Society

At T-Mobile, we invest in YOU! Our Total Rewards Package ensures that employees get the same big love we give our customers. All team members receive a competitive base salary and compensation package - this is Total Rewards. Employees enjoy multiple wealth-building opportunities through our annual stock grant, employee stock purchase plan, 401(k), and access to free, year-round money coaches. That's how we're UNSTOPPABLE for our employees! Job Overview The Senior Machine Learning (ML) Engineer focuses on enabling systems for coding, deploying, and maintaining large-scale machine learning models throughout their lifecycle. By combining software engineering principles and data science/machine learning knowledge, the ML Engineer develops the data processes that make ML models generally available for use in products for end-users and customers. The senior ML engineer should understand machine learning algorithms and be able to explain ML models to the business. Also, the experience in software engineering and various programming languages, including Python, SQL, and scalable computing such as Apache Spark are essential. This position also requires an in depth knowledge of the latest cloud computing technologies that are imperative for ML model development and deployment in this era. The chief contribution of the Senior ML Engineer is their ability to guide, influence, and inspire peak performance, innovation, and adoption of the latest software/ML engineering practices to enable AI technologies across the TMUS organization. Job Responsibilities: Mentor and guide a team of machine learning engineers that deliver novel, strategic scalable and optimized intelligent solutions with diverse, industry-leading skills in distributed machine Learning workflows/lifecycle, edge AI, data engineering, and tools both in the cloud and on-device. Assemble large, complex data sets that meet functional/ non-functional business requirements for machine learning. Collaborate with data science, data engineering, and product teams on defining, architecting, and building data ingestion systems and model training pipelines from experimentation to deployment, monitoring, and continuous performance improvement. Establish processes and standard methodologies to help grow the ML Engineering team. Communicate complex processes to business leaders. Develop an in depth understanding of business processes that are vital to solve analytics problems by understanding them and developing solutions catered to solve business problems / provide meaningful results. Education:Bachelor's Degree Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required) Master's/Advanced Degree Computer Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Preferred) Work Experience:4-7 years Data Engineering, Data Science (Required) 4-7 years Experience building and optimizing 'big data' data pipelines, architectures and data sets using SQL, Teradata, Oracle or similar (Required) 4-7 years Experience in Apache Spark and Databricks (Preferred) 4-7 years Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement (Required) 4-7 years Experience in languages such as Python/R, Java/Scala, and/or Go (Required) 4-7 years Experience delivering high-quality ML models in production (Required) Experience with real-world MLOps and knowledge of working in cloud-computing environment like Azure, AWS or Google Cloud (Preferred) Experience in the telecom industry (Preferred) Knowledge, Skills and Abilities:Programming Advanced SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases (Required) Big Data Solid understanding of machine