Data Scientist (All Levels)

Southern Company•Atlanta, GA

5h•Hybrid

About The Position

This position develops and applies advanced analytics, machine learning, AI, and modern data engineering practices to support structured pricing products, forecasting, valuation, and enterprise data platforms. The role designs, builds, and maintains analytical models, scalable data pipelines, curated data assets, and AI-enabled tools that deliver timely, accurate insights and automation. The position is accountable for improving data quality, platform reliability, model performance, governance, explainability, and scalability across markets, and partners across the enterprise to deploy secure, production-ready data, analytical, and AI solutions. This position may be filled under the following Grade Levels: Data Scientist (GL – 04), Senior Data Scientist (GL – 05), Staff Data Scientist (GL – 06), or Principal Data Scientist (GL – 07). Individuals who demonstrate advanced skills in work experience, education, and certifications may be eligible for the more advanced position.

Requirements

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.
Basic understanding of data engineering concepts, including data pipelines, data quality validation, and cloud-based analytics platforms.
Ability to develop or support reusable data transformations and follow established data engineering standards.
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).
Experience developing and maintaining production data pipelines using Databricks, Spark, Azure Data Factory, or comparable cloud technologies.
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).
Understanding of modern data engineering practices, including data ingestion, transformation, orchestration, data quality monitoring, and pipeline automation.
Experience building reusable, curated datasets and supporting production analytics environments.
Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field.
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.
Experience designing and implementing scalable data pipelines and cloud-based data solutions in Databricks and Azure environments.
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.
Strong understanding of data engineering concepts, including ETL/ELT, data modeling, pipeline orchestration, and data quality management.
Hands-on experience with Delta Lake, Spark optimization, Azure Data Factory, and cloud-native data platforms.
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.
Demonstrated experience leading enterprise data platform and data engineering initiatives in Databricks and Azure environments.
Expert-level modeling breadth (e.g., 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.
Expert understanding of modern data architecture, data modeling, ETL/ELT design, pipeline orchestration, and cloud-based data engineering practices.
Deep experience with Databricks, Azure Data Factory, Delta Lake, Spark, and related cloud technologies.
Ability to establish data engineering standards, data quality frameworks, observability, and operational practices for scalable analytics and AI solutions.

Nice To Haves

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

Responsibilities

Analyze and organize customer/market data for pricing, planning, and forecasting.
Support the development and validation of reusable data pipelines and curated datasets under guidance.
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.
Assist with monitoring data pipeline results, investigating data quality issues, and documenting corrective actions.
Design and develop statistical and ML models for business problems (forecasting, valuation, segmentation, anomaly detection).
Build and maintain scalable analytical and data engineering pipelines, reusable curated datasets, and feature assets; implement validation and data quality checks.
Develop and optimize data ingestion, transformation, and orchestration processes within Databricks and Azure environments.
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.
Lead end-to-end delivery of advanced analytics and AI solutions (design, build, deploy and monitor).
Design, develop, and maintain scalable cloud-based data pipelines and curated datasets that support analytics, reporting, AI, and business operations.
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.
Partner with IT and enterprise data teams to improve platform reliability, performance, security, and data governance.
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.
Provide strategic direction for SouthStar’s cloud data platform architecture, ensuring alignment across data engineering, analytics, and AI capabilities.
Establish data engineering design standards, reusable patterns, and operating practices for secure, reliable, and scalable data products.