Principal Data Scientists

T-Mobile•Bellevue, WA

21h•Hybrid

About The Position

Principal Data Scientists located in Bellevue, WA will implement and maintain modeling pipelines in Python, ensuring statistical accuracy and version control in collaboration with data engineering teams. Position duties and responsibilities include, but are not limited to: Communicate complex findings clearly to technical and non-technical stakeholders through presentations, documentation, and data visualizations that support decision-making. Stay current with advances in forecasting, attribution modeling, and statistical methods by engaging in professional development and applied learning. 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 are used to inform enterprise-level planning and budgeting decisions. 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. Guide the development and deployment of scalable modeling pipelines in Python, providing oversight to ensure reproducibility, rigor, and operational readiness. Mentor and review the work of junior data scientists providing methodological direction, feedback, and quality control. 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. 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.

Requirements

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.
Working independently to identify new segmentation opportunities using statistical methods including decision tree, clustering, leading to enhancements to decision process and policies.
Developing predictive analytical models using the appropriate statistical methodologies, including logistics regression, experimental design, and hypothesis testing.
Extracting, loading, and transforming data from multiple sources necessary for statistical, reporting and ad-hoc analysis.
Building complex machine learning algorithms with automated model parameter tuning.
Working with a cloud computing environment including Azure Databricks and AWS.
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.
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.
At least 18 years of age
Legally authorized to work in the United States

Responsibilities

Implement and maintain modeling pipelines in Python, ensuring statistical accuracy and version control in collaboration with data engineering teams.
Communicate complex findings clearly to technical and non-technical stakeholders through presentations, documentation, and data visualizations that support decision-making.
Stay current with advances in forecasting, attribution modeling, and statistical methods by engaging in professional development and applied learning.
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.
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.
Guide the development and deployment of scalable modeling pipelines in Python, providing oversight to ensure reproducibility, rigor, and operational readiness.
Mentor and review the work of junior data scientists providing methodological direction, feedback, and quality control.
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.