Senior Data Science Engineer

T-Mobile T-Mobile · Telecom · Philadelphia, PA +1

Senior Data Science Engineer at T-Mobile Advertising Solutions, focusing on building privacy-first advertising products using ML, large-scale data processing, and cloud technologies. The role involves leading end-to-end development of ML and data products, building scalable pipelines, applying statistical methods, writing production-quality code, and collaborating across teams. Requires experience in ML/DL deployment, MLOps, big data, and cloud platforms.

What you'd actually do

  1. Lead the end-to-end development of machine learning and data products aligned to business objectives, from problem framing through deployment and monitoring.
  2. Build scalable data, training, and inference pipelines using distributed processing and cloud technologies.
  3. Apply statistical methods, experimentation, and validation frameworks to ensure solution quality and business impact.
  4. Write production-quality code and contribute to engineering best practices, including testing, CI/CD, and observability.
  5. Collaborate across engineering, product, and business teams while leading other engineers and data scientists.

Skills

Required

  • Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience
  • Quantitative Discipline (math, statistics, economics, computer science, physics, engineering, etc.)
  • 4-7 years experience building and deploying machine learning and deep learning solutions at scale
  • familiarity with MLOps and DevOps practices and tools
  • 4-7 years Experience working within big data architecture, modern analytical data platforms, and large-scale data warehousing technologies (e.g. BigQuery, Snowflake, Redshift)
  • 4-7 years Experience working with large-scale distributed data systems and cloud platforms (e.g. SQL, Python, Scala, AWS)
  • 4-7 years Experience solving complex data, machine learning, or algorithmic challenges in production environment using modern engineering practices
  • Strong background in AI/ML, data structures, statistical modeling, optimization algorithms, big data, and design thinking
  • Advanced knowledge of cloud-based services (GCP, AWS)
  • Python, PySpark and related Python libraries (e.g. pandas, scikit-learn, scipy, numpy) for advanced data science tasks
  • Hands-on implementation and architectural familiarity with streaming data, relational and non-relational databases, and distributed processing technologies
  • Experience operating production machine learning and data systems in cloud and containerized environments

Nice to have

  • AdTech and GIS or geospatial data processing

What the JD emphasized

  • production-quality code
  • production environment
  • operating production machine learning and data systems

Other signals

  • building privacy-first advertising products powered by advanced machine learning
  • building AI and ML systems that directly impact our customers and business
  • Lead the end-to-end development of machine learning and data products aligned to business objectives, from problem framing through deployment and monitoring.
  • Build scalable data, training, and inference pipelines using distributed processing and cloud technologies.