Sr Data Scientists

T-Mobile•Bellevue, WA

7d•$166,036 - $171,036•Hybrid

About The Position

T-Mobile is hiring a Senior Data Scientist in Bellevue, Washington, to develop forecasting models that predict marketing performance, the impacts of budget allocation, and long-term customer acquisition trends. Telecommuting is permitted, but the employee must work from the Bellevue worksite at least 3–4 days per week. No national or international travel is anticipated, and travel is not required. The position has a 40-hour workweek. The role uses statistical and machine-learning models to support marketing measurement and business planning. The Senior Data Scientist monitors and troubleshoots models, investigates changes in model accuracy, runs scenario-based forecasts, and communicates insights to marketing, finance, and analytics teams. The role also collaborates with data engineers to scale and automate production deployment and contributes technical advancements in AI and machine learning for marketing measurement. The primary education and experience requirement is a master’s degree or foreign equivalent in Business Analytics, Economics, Engineering, Physics, Mathematics, Statistics, or a related field, plus three years of relevant work experience. Alternatively, candidates may qualify with a bachelor’s degree or foreign equivalent in one of those fields and five years of relevant work experience. The position requires experience with Python and R, SQL, cloud platforms, marketing and media analytics, statistical and machine-learning model validation, experimentation, and dashboard and report development. The listed annual salary range is $166,036–$171,036. Actual pay depends on factors including work location, qualifications, and experience. Employees in regular, non-temporary roles may be eligible for an annual bonus or other role-based incentives; employees are also eligible for an annual stock grant. T-Mobile describes benefits that may be available to eligible employees, including medical, dental, and vision insurance, a flexible spending account, 401(k), paid time off and holidays, paid parental and family leave, family-building benefits, childcare support, tuition assistance, disability and insurance options, discounts, pet insurance, and commuter and transit programs.

Requirements

Master’s degree or foreign equivalent in Business Analytics, Economics, Engineering, Physics, Mathematics, Statistics, or a related field, plus three years of relevant work experience; alternatively, a bachelor’s degree or foreign equivalent in one of these fields plus five years of relevant work experience.
Experience using Python and R to build, train, validate, and maintain statistical and machine-learning models in production, including diagnosing failures and performance degradation.
Experience with SQL and AWS, GCP, and Azure for scalable data ingestion, transformation, and analytical workflows.
Experience in marketing and media analytics, including media attribution using Google Marketing Platform and Adobe Analytics, and causal inference methods such as Bayesian structural time series, media holdout testing, and advanced media mix optimization.
Ability to interpret predictive analytics from machine-learning and statistical modeling frameworks, including regression and decision-tree techniques, using goodness-of-fit metrics, drift diagnostics, scenario testing, and sensitivity analysis.
Experience designing and executing statistical validation, hypothesis testing, and experimentation frameworks using Python- and R-based statistical libraries and controlled A/B testing to assess model performance, quantify uncertainty, and support business decisions.
Experience building business reports and dashboards using Tableau or Power BI to standardize reporting and identify optimization opportunities.
At least 18 years of age.
Legally authorized to work in the United States.

Responsibilities

Use Python to diagnose and resolve statistical model failures caused by data or code issues.
Use Python and SQL to monitor statistical model outputs, assess accuracy, and detect signs of drift.
Investigate causes of changes in model accuracy and flag concerns.
Run and analyze scenario-based forecasts to support business planning using Tableau and Power BI.
Collaborate with marketing, finance, and analytics teams to translate model insights into business strategies that improve ROI.
Work with data engineers to scale and automate model deployment and integrate machine-learning solutions into production environments.
Keep current with AI and machine-learning innovations in marketing measurement and drive technical advancements and thought leadership within the team.