Senior Data Scientist at AT&T
Atlanta, GA
$139,000–$233,500from the description
Jul 15, 2026
Atlanta, GA
Jul 21, 2026
What this job asks for AI summary
A senior data science role embedded within finance operations, focused on building and deploying machine learning and generative AI models to support treasury, billing, and corporate financial planning functions. Day-to-day work spans the full ML lifecycle — data extraction and preparation, feature engineering, model development and tuning, production deployment, and MLOps monitoring. Suits candidates with a quantitative graduate degree and hands-on experience across supervised, unsupervised, and generative AI methods.
Mid level · 3+ years · Dallas-Fort Worth-Arlington, TX · Master's required · Full-time
Advertised as Senior, but the requirements read as Mid.
“or” means any one of them counts — you don't need all of them.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 65 people in the Dallas-Fort Worth-Arlington, TX area plausibly meet what this posting asks for (data scientists). range 40–110
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $130,559 (middle half $92,500–$149,977). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (8)
The role is posted in two metros — Atlanta, GA and Dallas-Fort Worth, TX. Dallas CBSA is used as the primary; Atlanta (CBSA 12060) is an equally valid location.
The JD titles this 'Senior Data Scientist' but requires only 3+ years of experience and a Master's degree. The scope (leading moderate-sized projects, frequent senior leadership interaction, no direct reports) is consistent with a solid Mid-level practitioner despite the Senior label — hence the seniority assessment differs from the advertised title.
A Master's degree in a quantitative field is explicitly stated as required (not 'or equivalent experience'), so the degree requirement is set to Masters.
Coding proficiency in at least one data science language (Python, R, Scala) is a hard gate; Python is listed first and is the most common, with R and Scala captured as alternatives.
ML packages/libraries (Spark, scikit-learn, pandas, PyTorch, TensorFlow, Keras, TidyVerse, Shiny, AutoML) are listed in the requirements section but framed as 'and/or' — indicating familiarity with the ecosystem rather than every tool. Spark, scikit-learn, and pandas are marked required as the most universally expected; PyTorch/TensorFlow/Keras are grouped as preferred alternatives given the 'and/or' framing.
Generative AI capabilities (GANs, VAEs, Transformers, RAG, prompt engineering, image generation tools) appear in a dedicated subsection of the responsibilities narrative rather than a hard-gate qualifications block, so they are marked preferred.
MLflow and Databricks are called out by name in the requirements-style workflow description ('uses concepts like mlflow', 'IDEs such as Databricks Workspaces') and are treated as hard gates.
The 'machine learning', 'deep learning', and 'NLP' skills represent algorithm-category proficiency explicitly required in the qualifications section; they are retained as named capability areas because the JD gates on them by category.
Read the full posting
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