Senior Data Scientist

AT&T AT&T · Telecom · Dallas, TX +1

Senior Data Scientist role focused on developing and implementing generative AI models, including fine-tuning, agentics, RAG, and prompt engineering, for AT&T Finance Operations. The role involves the full AI workflow from data extraction and feature engineering to model deployment and monitoring, with a strong emphasis on translating business problems into actionable insights using machine learning and big data technologies.

What you'd actually do

  1. Develop and implement generative AI models, focusing on creating new content or augmenting existing data.
  2. Codes solutions 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.
  3. Build, evaluate, and optimize machine learning models through hyperparameter tuning.
  4. Collect data from various structured and unstructured sources (datalakes, databases, data warehouses, on cloud, internal, external) and ensure its quality for analysis through cleaning and preprocessing.
  5. Create visualizations and reports for stakeholders while working closely with cross-functional teams to align efforts with business objectives.

Skills

Required

  • Python
  • R
  • Scala
  • Spark
  • SciKitLearn
  • Pandas
  • PyTorch
  • Tensorflow
  • Keras
  • AutoML tools
  • mlflow
  • Databricks Workspaces
  • Visual Studio Code
  • Supervised Learning
  • Unsupervised Learning
  • Optimization Algorithms
  • Deep Learning
  • AI-Computer Vision
  • Natural Language Processing
  • Deep Reinforcement Learning
  • Search Algorithms
  • AI- Knowledge Graphs
  • GANs
  • VAEs
  • Transformers
  • Fine-Tuning
  • Agentics
  • Prompt Engineering
  • RAG
  • Text Generation
  • Image Generation
  • DALL-E
  • Stable Diffusion

Nice to have

  • Treasury/Payments
  • Billing Operations
  • Corporate Financial Planning
  • TidyVerse
  • Shiny
  • ggplot
  • D3.js
  • model interpretation
  • model retraining
  • model decay
  • custom Machine Learning (ML)
  • model serving
  • model optimization
  • model monitoring
  • model explainability
  • model reliability

What the JD emphasized

  • Significant experience with these partners, their KPIs, processes and business challenges is preferred.
  • Coding proficiency required in at least one data science language (Python, R, Scala, etc.), as well as expertise with modern ML packages and libraries (Spark, SciKitLearn, Pandas, PyTorch, TidyVerse, Tensorflow, Keras, Shiny, and/or AutoML tools).
  • Highly proficient in the full AI workflow such as (1) data extraction, cleansing, feature engineering, exploratory data analysis, model selection/creation, hyper-parameter tuning, model interpretation, model retraining and (2) Uses concepts like mlflow to log metrics.
  • 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, and AI- Knowledge Graphs.

Other signals

  • Develop and implement generative AI models
  • Fine-Tuning
  • Agentics
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Text Generation
  • Image Generation