Principal Data Scientists

T-Mobile T-Mobile · Telecom · Bellevue, WA +1

Principal Data Scientists at T-Mobile will implement and maintain modeling pipelines in Python, ensuring statistical accuracy and version control. They will 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. These models will be used to inform enterprise-level planning and budgeting decisions. The role also involves contributing to the design, innovation, and refinement of media attribution models to evaluate marketing effectiveness and align attribution insights with forecasting strategies. Additionally, they will guide the development and deployment of scalable modeling pipelines, mentor junior data scientists, and collaborate cross-functionally with marketing, analytics, and data engineering teams.

What you'd actually do

  1. 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.
  2. Communicate complex findings clearly to technical and non-technical stakeholders through presentations, documentation, and data visualizations that support decision-making (10%).
  3. Guide the development and deployment of scalable modeling pipelines in Python, providing oversight to ensure reproducibility, rigor, and operational readiness (15%).
  4. 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%).
  5. Mentor and review the work of junior data scientists providing methodological direction, feedback, and quality control (10%).

Skills

Required

  • 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

Nice to have

  • Stay current with advances in forecasting, attribution modeling, and statistical methods by engaging in professional development and applied learning

What the JD emphasized

  • advanced statistical and machine learning models
  • forecasting strategies
  • scalable modeling pipelines

Other signals

  • implement and maintain modeling pipelines
  • design, lead and innovate the development of advanced statistical and machine learning models
  • forecast business outcomes
  • inform enterprise-level planning and budgeting decisions
  • evaluate marketing effectiveness
  • guide the development and deployment of scalable modeling pipelines