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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr Specialist Data Scientist - **Company:** AT&T Inc. - **Location:** Plano, TX, United States - **Experience:** Expert - **Salary:** $132,600.0 - $192,100.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Big Data, Cloud Computing, Computer Programming, Information Engineering, Extract Transform Load (ETL), Data Structures, Data Visualization, Graph Database, Apache Hadoop, Python (Programming Language), Search Algorithms, Machine Learning, Natural Language Processing, NoSQL, Power BI, Tensorflow, SQL Databases, Tableau (Software), Reinforcement Learning, Jupyter Notebook, Supervised Learning, Google Cloud, Pytorch, Apache Spark, Deep Learning, Keras, Git, Pandas, Matplotlib, Containerization, Scikit Learn, Optimization Algorithms, Machine Learning Operations, Software Version Control, Docker, Unsupervised Learning, Databricks - **Published:** September 5, 2026 - **Apply:** https://www.jofdav.com/jobs/59569757-sr-specialist-data-scientist ## About the Role REQUIREMENTS: Requires a Bachelor's degree, or foreign equivalent degree in Data Science, Math, Statistics, Engineering, or Physics and 2 years of experience in the job offered or 2 years of experience in a related occupation applying knowledge in statistical design of experiments and applying knowledge of statistical concepts; applying knowledge in algorithm categories including Supervised Learning, Unsupervised Learning, Optimization Algorithms, Deep Learning, AI-Computer Vision, Natural Language Processing, Deep Reinforcement Learning, Search Algorithms, and AI- Knowledge Graphs; applying Coding knowledge required in at least one of the following data science languages: Python, R, Scala, and SQL; applying knowledge with modern ML packages and libraries including SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and AutoML tools. ## Description DUTIES: Design, build, and analyze large and complex data sets from various structured and unstructured sources by leveraging your strategic thinking about data use and design. Apply statistical techniques and advanced visualization tools to understand data structure, trends, and relationships while creating new features or modify existing ones to enhance machine learning model performance. Solve business problems, develop income-generating models, and identify opportunities for cost savings by optimizing business processes. Apply knowledge of Python, R, Scala, or SQL. Apply experience with big data platforms including Hadoop, Spark, and Databricks. Apply knowledge of cloud technologies including AWS, Google Cloud, and Azure. Utilize machine learning frameworks including Scikit- Learn, Pandas, PyTorch, TensorFlow, Keras, and AutoML tools. Utilize visualization tools including Matplotlib, Seaborn, Tableau, and Power BI. Apply experience with ETL pipelines, workflow schedulers including Apache Airflow, version control systems including Git, and knowledge of both relational and NoSQL databases. Apply knowledge with MLOps practice and containerization tools including Docker, and collaborative environments including Jupyter Notebooks. Apply knowledge in statistical design of experiments, algorithm categories, and modern data engineering practices to drive impactful business insights and solutions. Apply knowledge in statistical design of experiments. Apply knowledge of statistical concepts. Apply knowledge in algorithm categories including Supervised Learning, Unsupervised Learning, Optimization Algorithms, Deep Learning, AI-Computer Vision, Natural Language Processing, Deep Reinforcement Learning, Search Algorithms, and AI Knowledge Graphs. 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