Principal Data Scientists

are used to inform enterprise-level planning and budgeting decisions (30%). Contribute to the design, innovation and refinement of media attribution models including Marketing Mix Modeling and Multi-Touch Attribution to evaluate marketing effectiveness and align attribution insights with forecasting strategies (10%). Guide the development and deployment of scalable modeling pipelines in Python, providing oversight to ensure reproducibility, rigor, and operational readiness (15%). Mentor and review the work of junior data scientists providing methodological direction, feedback, and quality control (10%). Collaborate cross-functionally with marketing, analytics, and data engineering teams to ensure that forecasting and attribution outputs meet business needs and are integrated into decision-making processes (10%). Telecommuting is permitted, but applicant must work from the worksite location at least 3-4 days per week. No additional national or international travel is anticipated.Experience

and education requirements: PRIMARY REQUIREMENTS: Master's degree in Applied Economics, Economics, Mathematics, Operations Research, Statistics, Finance, or related, or its foreign equivalent and 5 years of relevant work experience. ALTERNATIVE REQUIREMENTS: Bachelor's degree in Applied Economics, Economics, Mathematics, Operations Research, Statistics, Finance, or related, or its foreign equivalent and 7 years of relevant work experience. In addition, the following skills are required: (1) Using SQL and Python or other statistical/analytical programming languages to manipulate large amounts of data, extract key insights from the data, and then clearly and concisely communicate actionable recommendations based upon insight; (2) Working independently to identify new segmentation opportunities using statistical methods including decision tree, clustering, leading to enhancements to decision process and policies; (3) Developing predictive analytical models using the appropriate statistical methodologies, including logistics regression, experimental design, and hypothesis testing; (4) Extracting, loading, and transforming data from multiple sources necessary for statistical, reporting and ad-hoc analysis; (5) Building complex machine learning algorithms with automated model parameter tuning; and (6) Working with a cloud computing environment including Azure Databricks and AWS. Washington Pay Range: $196,914.00 to $224,000.00/year. The pay range above is the general base pay range for a successful candidate in the state listed. The successful candidate's actual pay will be based on various factors, such as work location, qualifications, and experience. At T-Mobile, employees in regular, non-temporary roles are eligible for an annual bonus or periodic sales incentive or bonus, based on their role. Most Corporate employees are eligible for a year-end bonus based on company and/or individual performance and which is set at a percentage of the employee's eligible

T-Mobile USA, Inc. seeks Principal Data Scientists in Bellevue, WA to implement and maintain modeling pipelines in Python, ensuring statistical accuracy and version control in collaboration with data engineering teams (10%). Communicate complex findings clearly to technical and non-technical stakeholders through presentations, documentation, and data visualizations that support decision-making (10%). Stay current with advances in forecasting, attribution modeling, and statistical methods by engaging in professional development and applied learning (5%). Design, lead and innovate the development of advanced statistical and machine learning models to forecast business outcomes such as service activations, digital and retail traffic, and related KPIs. Methods include classic machine learning methods, like statistical regression analysis and dimensionality reduction, and innovative methods, like ensemble models and deep learning. Models are developed with a focus on strategic scalability and