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About This Role
Data Scientist (All Levels)
JOB SUMMARY:
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.
JOB REQUIREMENTS: (Education, Experience, Knowledge, Skills )
Data Scientist (Level 04)
Education
- Required: Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).
- Preferred: Master’s degree (or in progress) in a quantitative discipline
Experience
- 0-2 years of experience (including internships/co-ops) in analytics, data, or modeling.
- Preferred: Exposure to energy/utility markets or pricing/forecasting concepts (through coursework or experience).
- Preferred: Exposure to Databricks, Spark, Azure, or similar modern data and analytics technologies through coursework, internships, or work experience.
Knowledge/Skills
- 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.
Senior Data Scientist (Level 05)
Education
- Required: Bachelor’s degree in a quantitative field (e.g., mathematics, statistics, economics, data science, computer science, or similar).
- Preferred: Master’s degree in analytics, statistics, data science, computer science, or similar.
Experience
- 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.
Knowledge/Skills
- 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.
Staff Data Scientist (Level 06)
Education
- Required: Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field
- Preferred: PhD (or in progress) in one of the above disciplines
Experience
- 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.
Knowledge/Skills
- 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.
Principal Data Scientist (Level 07)
Education
- Required: Master’s degree in analytics, statistics, data science, computer science, or a related quantitative field
- Preferred: PhD in one of the above disciplines; Project Management Professional (PMP) or equivalent leadership certification
Experience
- 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.
Knowledge/Skills
- 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.
MAJOR JOB RESPONSIBILITIES:
Data Scientist (Level 04)
Summary: Applies analytical and ML techniques under guidance to support forecasting/valuation and build reusable, well-documented analyses.
- 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.
Senior Data Scientist (Level 05)
Summary: Independently designs and delivers advanced analytics and machine learning solutions and reusable pipelines for business use cases
- 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.
Staff Data Scientist (Level 06)
Summary: Leads advanced analytics and AI initiatives end-to-end, establishing standards and ensuring scalable, governed delivery.
- 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.
Principal Data Scientist (Level 07)
Summary: Serves as SouthStar’s senior technical authority for Data, Analytics and AI strategy, setting standards and priorities to ensure secure, scalable, and production ‑ ready solutions.
- 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.
Location and Work Environment
This position is based at SouthStar's corporate office in Atlanta, Georgia, with a hybrid work schedule of four days in-office and one day remote per week (subject to change per business needs). The work environment is dynamic, fast-paced, and challenging with competing demands.
Disclaimer:
This information describes the general nature and level of work performed by employees in this job. The description is not designed to be a comprehensive inventory of duties, responsibilities and qualifications required in the job. Reasonable accommodations may be made to qualified disabled individuals for performance of essential duties and responsibilities.
About Southern Company Gas
Southern Company Gas is a wholly owned subsidiary of Atlanta-based Southern Company (NYSE:SO), America’s premier energy company. Southern Company Gas serves approximately 4.2 million natural gas utility customers through its regulated distribution companies in four states and approximately 600,000 retail customers through its companies that market natural gas. Other nonutility businesses include investments in interstate pipelines and ownership and operation of natural gas storage facilities. For more information, visit southerncompanygas.com .
Southern Company (NYSE: SO ) is a leading energy provider serving 9 million customers across the Southeast and beyond through its family of companies. Providing clean, safe, reliable and affordable energy with excellent service is our mission. The company has electric operating companies in three states, natural gas distribution companies in four states, a competitive generation company, a leading distributed energy solutions provider with national capabilities, a fiber optics network and telecommunications services. Through an industry-leading commitment to innovation, resilience and sustainability, we are taking action to meet customers' and communities' needs while advancing our goal of net-zero greenhouse gas emissions by 2050. Our uncompromising values ensure we put the needs of those we serve at the center of everything we do and are the key to our sustained success. We are transforming energy into economic, environmental and social progress for tomorrow. Our corporate culture has been recognized by a variety of organizations, earning the company awards and recognitions that reflect Our Values and dedication to service. To learn more, visit www.southerncompany.com .
Southern Company invests in the well-being of its employees and their families through a comprehensive total rewards strategy that includes competitive base salary, annual incentive awards for eligible employees and health, welfare and retirement benefits designed to support physical, financial, and emotional/social well-being. This position may also be eligible for additional compensation, such as an incentive program, with the amount of any bonus/awards subject to the terms and conditions of the applicable incentive plan(s). A summary of the benefits offered for this position can be found here https://seo.nlx.org/southernco/pdf/SOCO-Benefits.pdf . Additional and specific details about total compensation and benefits will also be provided during the hiring process.
Southern Company is an equal opportunity employer where an applicant's qualifications are considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, genetic information, sexual orientation, gender identity or expression, or any other basis prohibited by law.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 8,943 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Southern Company, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,406 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Southern Company AI Hiring
Southern Company has 1 open AI role right now. They're hiring across Data Scientist. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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