Data Scientist (All Levels)

Southern Company • Atlanta, GA

3d • Hybrid

About The Position

This position develops and applies advanced analytics, machine learning, and AI to support structured pricing products, forecasting, and valuation. The role builds and maintains models, data assets, and AI-enabled tools that deliver timely, accurate insights and automation to improve decision-making and elevate workforce capability. The position is accountable for improving model performance, governance, explainability, and scalability across markets, and partners across the enterprise to deploy reliable analytical and AI solutions.

Requirements

• Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field
• 10+ years of experience in analytics/data science, including a strong track record delivering enterprise-scale data and AI solutions.
• Extensive experience manipulating large data using SQL and platforms such as Databricks/Spark/Azure/AWS.
• Demonstrated success defining and scaling AI capabilities, frameworks, or platforms with strong execution.
• Expert-level modeling breadth (e.g., Natural Language Processing (NLP), deep learning, Bayesian methods, clustering, neural networks) and strong statistical foundations.
• Proven ability to define reusable AI/ML frameworks, standards, and governance guardrails.
• Experienced in complex integrations/migrations across data sources and platforms such as Databricks, Azure; sets architectural direction in partnership with IT
• Demonstrated leadership in analytical model design/ development/ testing/ troubleshooting/ documentation for complex analytical systems
• Strong stakeholder management capabilities and a proven ability to align teams
• 5-10 years of experience in analytics/data science, modeling, and large-scale data work.
• Hands-on experience delivering machine learning/AI solutions and/or production-level model deployment.
• Experience manipulating large databases using SQL and platforms such as Databricks/Spark/Azure/AWS.
• Advanced modeling breadth (supervised/unsupervised methods) and strong statistical foundations.
• Ability to design validation, monitoring, and retraining plans (drift detection, performance thresholds).
• Strong troubleshooting and documentation skills for complex analytical systems.
• Technical leadership and stakeholder management; able to drive alignment across teams.
• Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).
• 3-5 years' experience in analytics/modeling and data processing
• Demonstrated ability to build and manage models in a business environment.
• Experience working with large data using SQL and modern analytics platforms (e.g., Databricks/Spark/Azure/AWS).
• Strong programming skills in Python or R; solid SQL proficiency.
• Experience with feature engineering, model evaluation, and performance tuning.
• Experience with dashboards/visualizations (e.g., Power BI, Tableau, SSRS) to communicate insights.
• Strong understanding of data governance basics (access, quality checks, and documentation).
• Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).
• 0-2 years of experience (including internships/co-ops) in analytics, data, or modeling.
• Working knowledge of SQL and relational databases (e.g., SQL Server, Oracle, MySQL).
• Hands-on programming for analysis (Python or R) and basic ML/statistical modeling.
• Ability to follow established standards for documentation, validation, and reproducibility.
• Able to communicate results clearly to technical and non-technical stakeholders.

Nice To Haves

• PhD in one of the above disciplines; Project Management Professional (PMP) or equivalent leadership certification
• PhD (or in progress) in one of the above disciplines
• Master’s degree in analytics, statistics, data science, computer science, or similar.
• Master’s degree (or in progress) in a quantitative discipline
• Exposure to energy/utility markets or pricing/forecasting concepts (through coursework or experience).

Responsibilities

• Accountable for domain AI outcomes and risk posture, including final technical approval for production readiness
• Prioritize AI use cases based on business value, feasibility, and risk.
• Define and enforce SouthStar standards for model development, validation, monitoring, documentation, and responsible AI use, aligned with enterprise frameworks.
• Establish reusable AI/ML frameworks, templates, and best practices to accelerate delivery across teams.
• Lead cross-functional delivery of production AI solutions (automation, forecasting, decision support, AI assistants).
• Drive workforce enablement (training, playbooks, coaching) to elevate AI adoption and productivity.
• Provide technical direction across multiple teams and functions, leading through influence rather than formal authority.
• Mentor junior staff on modeling practices and documentation; conduct technical reviews and guide complex problem-solving
• Stay current on GenAI, NLP, and advanced ML trends and assess their applicability to SouthStar’s business.
• Lead end-to-end delivery of advanced analytics and AI solutions (design, build, deploy and monitor).
• Define modeling standards, validation approaches, and monitoring thresholds; ensure explainability and audit readiness.
• Drive adoption by integrating models and AI tools into business workflows and decision processes.
• Partner with leadership to prioritize use cases, manage tradeoffs, and quantify business impact.
• Continuously improve data quality and governance practices to support scalable AI across markets.
• Design and develop statistical and ML models for business problems (forecasting, valuation, segmentation, anomaly detection).
• Build scalable analytical pipelines and reusable datasets/features; implement validation checks.
• Create AI-enabled analytical tools (e.g., guided Q&A over curated data, automated insight generation) with measurable value.
• Develop dashboards and stakeholder-ready outputs; explain model results and tradeoffs.
• Collaborate cross-functionally to define requirements, success metrics, and adoption approach.
• Own delivery for assigned workstreams.
• Analyze and organize customer/market data for pricing, planning, and forecasting.
• Maintain and validate existing models and recurring reports; troubleshoot data issues.
• Develop baseline statistical or ML models under guidance (e.g., regression, classification, forecasting).
• Use approved AI tools to automate routine analysis and reporting (e.g., templated notebooks, prompt-driven summaries).
• Document assumptions, code, and data lineage; support audit and review requests.
• Continuously identify small improvements to data quality, model performance, and efficiency.

Benefits

• competitive base salary
• annual incentive awards for eligible employees
• health, welfare and retirement benefits designed to support physical, financial, and emotional/social well-being
• incentive program

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level