Sr Specialist-Data Scientist

Key Skills

Java Programming, Python Programming, SQL, Apache Kafka, Scala Programming, Machine learning techniques, Data science techniques, Deep Learning, Natural Language Processing (NLP), Pandas, PyTorch, TensorFlow, Apache Hive, Snowflake, Scikit-learn, R Programming, PySpark, R-Shiny

Job Description

Join AT&T 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 AT&T, you won’t just imagine the future – you’ll create it.

The Data Scientist translates 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.

 Key Roles and Responsibilities

Designs and builds analytical models using large and complex data sets from various structured and unstructured sources while thinking strategically about data use and model implementation. The 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.

Qualifications

• 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 a PhD or Master’s in a Quantitative Field of Study with no min experience.
• 2-3 years of hands-on data science experience in designing, developing, and delivering machine learning models (e.g., Classification and Clustering) with large volume of data
• Strong analytical skills to identify data patterns, trends, and relationships from multiple datasets, including data validation, exploratory data analysis, and data visualization
• Strong grasp of Predictive modeling concepts
• boosting / bagging
• probability calibration
• threshold selection
• Strong programming skills (e.g., Python and Hive/SQL) and business acumen to turn raw data into insights and perform feature engineering required.
• Experience working on Databricks, pyspark, Snowflake, Kafka streaming
• Strong communication skills to effectively communicate data insights & machine learning model results to technical and nontechnical people
• Proficient in statistical design of experiments (factorial, response surface modeling, Plackett-Burman, central composite designs, Box-Behnken, etc.) in order to validate proof of concepts in a production environment, protect against selection and/or emergent bias and to allow simultaneous trials without cross-trial interference.
• 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, as this position implements AI solutions within the target AI architecture led by the Data Council/Data Review Board.