Principal| Data Scientist

Join ATT and reimagine the communications and technologies that connect the world. We're committed to those who seek to discover the undiscoverable and dare to disrupt the norm. 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 ATT, you won't just imagine the future – you'll create it.

As a Senior Data Scientist, you will:
• Translate business problems to insights and codes solutions using the following typical workflow; data extraction, cleansing, feature engineering, exploratory data analysis, model selection/creation, hyper-parameter tuning, model interpretation, model retraining, business process and/or system implementations, high level proof of concept and trials, visualization, deployment to production, post deployment ML ops monitoring/diagnosis/resolutions.
• Develop solutions for customer care which improve first-call-resolution KPIs. Example solutions include conversation intelligence/transcript analysis (supervised unsupervised, turn-level) to uncover process, tool or skill training insights as well as propensity modeling to support call routing and on-screen agent support.
• 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.
• Validate proof of concepts in a production environment, protect against selection and/or emergent bias and to allow simultaneous trials without cross-trial interference.
• Organize and communicate insights from analysis of large data sets in an intuitive manner to non-technical business partners.
• Implement AI solutions within the target AI architecture led by the Data Council/Data Review Board.
• Proficient English Language Skill (Verbal and Writing).
• Proficient in statistical design of experiments.
• 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)
• Expertise with modern ML packages and libraries (SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and/or AutoML tools).
• Proficiency in cloud AI technologies.
• Education: Preferred Master's degree from an accredited University in a Quantitative field of study such as Data Science, Math, Statistics, Engineering or Physics.
• Experience: Typically requires 5-8 years Read more