Hybrid Sr Specialist Data Scientist na AT&T

This position requires office presence of a minimum of 5 days per week and is only located in the location(s) posted. No relocation is offered.

Join AT&T and reimagine the communications and technologies that connect the world. Our Chief Data Office is delivering innovative and reliable technology solutions to power differentiated, simplified customer experiences. Bring your bold ideas and fearless risk-taking to redefine connectivity and transform how the world shares stories and experiences that matter. When you step into a career with AT&T, you won’t just imagine the future-you’ll create it.

As a Senior Specialist Data Scientist, you’ll turn business challenges into actionable insights by building end-to-end solutions—from data extraction and cleansing to modeling, deployment, and post-launch monitoring.

What You Will Do

• The data models created may be used internally and by business units to solve business problems, create income generating models, or to identify improved business processes for cost savings opportunities.

• Designs, builds, and analyzes large (e.g. 100s of Terabytes or higher as technology advances) and complex data sets from various structured and unstructured sources while thinking strategically about data use and data design.

• Uses statistical techniques and visualization tools to understand the data's structure, trends, and relationships.

• Creates new features or modifies existing ones to improve the performance of machine learning models.

What You Will Bring

• Required Bachelor’s degree from an accredited University in a Quantitative field of study such as Data Science, Math, Statistics, Engineering or Physics  and 2-3 years of experience OR PhD or Masters in Quantitative Field of Study with no min experience.

• Proficient in statistical design of experiments (factorial, response surface modeling, Plackett-Burman, central composite designs, Box-Behnken, etc.)

• Strong grasp of basic statistical concepts and a strong understanding of the theoretical underpinnings of any techniques being utilized.

• Proficiency in algorithm categories such as Supervised Learning, Unsupervised Learning, Optimization Algorithms, Deep Learning, AI-Computer Vision, Natural Language Processing, Deep Reinforcement Learning, Search Algorithms, AI- Knowledge Graphs.

• Coding proficiency required in at least one data science language (Python, R, Scala, and SQL), as well as expertise with modern ML packages and libraries (SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and/or AutoML tools).

• Proficiency in cloud AI technologies desired.

• Ability to organize and crisply communicate insights from analysis of large data sets in an intuitive manner to non-technical business partners