> Markdown version of [/jobs/ext/4334262-t-ads-engineer-data-science](https://www.wearedevelopers.com/jobs/ext/4334262-t-ads-engineer-data-science). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # T-Ads Engineer, Data Science - **Company:** T-Mobile Us, Inc. - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $98,200.0 - $225,745.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Amazon Web Services, Architectural Patterns, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Continuous Integration, Data Visualization, Distributed Computing Environment, Apache Hadoop, Apache Hive, Python (Programming Language), Machine Learning, Modular Design, NumPy, Scala (Programming Language), SciPy, Software Deployment, Software Engineering, SQL Databases, Data Streaming, Google Cloud, Delivery Pipeline, Snowflake, Apache Spark, Deep Learning, Pandas, Containerization, Pyspark, Scikit Learn, Storage Technologies, Information Technology, Production Code, Apache Kafka, Non-relational Database, Machine Learning Operations, Software Version Control, Data Pipelines, Docker - **Published:** October 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=60c3438b2e80700b ## About the Role * Bachelor's Degree plus 2 years of related work experience OR combination of education and experience deemed equivalent (Required) * Acceptable areas of study include Quantitative Discipline (math, statistics, economics, computer science, physics, engineering, etc.) (Required) * 2-4 years Industry experience in predictive modeling, data science, and analysis in an ML engineer or data scientist role building and deploying ML/deep learning models (Required) * 2-4 years Experience with common AI/ML programming languages (e.g., SQL, Python, Scala) (Required) * 2-4 years Experience designing and working within big data architectures using distributed processing and storage technologies (e.g., Spark, Snowflake, Hadoop, Hive, Kafka), along with hands-on experience leveraging cloud platforms (e.g., AWS, GCP, Azure) to build scalable, reliable, and production-grade data and machine learning systems (Required) * 2-4 years Experience articulating and translating business questions and using statistical techniques to arrive at an answer using available data (Required) * 2-4 years Experience working with relational and non-relational databases using SQL (Required) * 2-4 years Experience in data visualization (Required) Knowledges, Skills, and Abilities: * Ability to rigorously structure ambiguous problems, evaluate competing technical approaches using statistical and systems reasoning, anticipate failure modes, and make defensible decisions aligned to business impact. (Required) * Ability to design statistically defensible evaluations, detect subtle validity issues, and quantify uncertainty in business-facing decisions. (Required) * Machine Learning: Ability to design and implement ML solutions within established system architectures, select appropriate model classes and loss functions aligned to business objectives, implement robust cross-validation and hyperparameter optimization strategies, productionize models via reproducible pipelines and CI/CD practices, and continuously monitor for drift, robustness issues, and system-level constraints (scalability, reliability, observability, and cost). (Required) * Advanced proficiency in Python and PySpark, including extensive use of data science and ML libraries (e.g., pandas, NumPy, SciPy, scikit-learn) to build scalable data and modeling solutions. (Required) * Demonstrated ability to design and implement robust, efficient, and maintainable code using sound software engineering principles (modular design, testing, version control, CI/CD), including experience packaging and deploying applications using containerization technologies such as Docker, with working knowledge of Scala in distributed Spark environments when required. (Required) * Strong communication skills, ability to work with cross functional teams. (Required) * At least 18 years of age * Legally authorized to work in the United States ## Description The T-Ads Engineer, Data Science builds and deploys production-grade machine learning models and data pipelines within established architectural patterns. This role translates defined business problems into scalable technical solutions and executes across the full ML lifecycle under appropriate technical guidance. The engineer implements statistically sound models, contributes to pipeline design, and ensures reliable production deployment using engineering best practices. Success is measured by high-quality execution, technical depth, and consistent delivery of well-engineered ML components that drive business value., * Owns delivery of well-scoped ML systems or major components of larger systems, operating within established architectural standards and technical direction. * Translates moderately complex business problems into technical approaches, proposing solution options within defined architectural constraints. * Implements statistically rigorous solutions, applying appropriate testing, validation, and evaluation techniques aligned to defined business metrics. * Designs, trains, tunes, and deploys ML/deep learning models using established techniques with evaluation frameworks aligned to business metrics. * Implements scalable training and deployment pipelines using PySpark, Snowpark or similar technologies. Understands batch/streaming, distributed processing and cloud technologies as required for projects. * Writes production-ready code in common data science languages like Python (including AI/ML libraries), SQL, Scala; enforces engineering standards including CI/CD and automated testing. * Collaborates across engineering, product, and business stakeholders. * Contributes to technical design documentation and actively participates in peer design reviews. * Applies lean development principles by delivering solutions iteratively, validating impact through measurable outcomes, incorporating stakeholder feedback, and continuously refining models and systems using data-driven build-measure-learn cycles.