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About This Role
WORK LOCATION
This position will be located at the Energy Center in Birmingham, AL. Candidates living outside of the Birmingham area may be considered with the expectation that they are able to successfully telework and are willing to travel to Birmingham four days per week.
JOB SUMMARY
Structuring and Analysis supports Commercial Operations and operating companies by providing solutions to complex, and often weather\-driven business challenges through advanced analytics and data science.
This position is responsible for collecting, organizing, mining, and analyzing complex datasets—including meteorological, atmospheric, and environmental data—to generate actionable insights that enable informed decision\-making and operational effectiveness. Specifically, this role leads and coordinates meteorology\-focused data science initiatives supporting Commercial Operations through weather\- and climate\-informed risk analysis, predictive modeling, and decision\-support tool development.
The position requires a strong working knowledge of applied meteorology, atmospheric science, mathematical and statistical methods, and modern data science practices. Success in this role also depends on the ability to manage multiple analytic initiatives, translate technical results into clear business insights, and partner effectively with stakeholders across Commercial Operations, Planning, and operating companies.
JOB RESPONSIBILITIES
- Lead and coordinate the development of meteorological and weather\-driven analytics initiatives, including internal tools, modeling approaches, and decision\-support methodologies.
- Partner with data scientists and meteorologists to design experiments and analyses that test assumptions related to weather, climate variability, and operational risk.
- Oversee development and application of predictive and prescriptive models incorporating numerical weather prediction outputs, historical weather data, and climate signals to support commercial and operational decisions.
- Translate complex meteorological and analytic outputs into clear, actionable insights for non\-technical stakeholders.
- Collaborate with end users to identify business needs, gaps, and opportunities for improved weather\- and climate\-informed analytics solutions.
- Provide leadership by coordinating cross\-functional teams, mentoring analysts, and promoting best practices in data science and applied meteorology.
- Stay current on emerging trends in meteorological modeling, climate risk analytics, machine learning, and data science technologies relevant to the electric utility industry.
- Ensure analytics solutions are well\-documented, maintainable, and aligned with business objectives and delivery timelines.
All major job responsibilities require strong quantitative, statistical, and analytical skills, combined with effective communication and project leadership capabilities.
JOB QUALIFICATIONS
Education:
- A degree in atmospheric science, meteorology, or a closely related meteorological analytical field is required.
- Additional degrees in quantitative finance, economics, mathematics, statistics, computer science, engineering, or similar analytical disciplines are a plus.
An advanced degree in any of these fields is preferred.
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Experience / Skills:
- Advanced statistical analysis skills, including multivariable analysis, optimization, and predictive modeling.
- Strong working knowledge of applied meteorology and experience evaluating and using numerical weather prediction models (e.g., GFS, NAM, HRRR, ECMWF).
- Strong programming and technical competency in large\-scale data analysis using Python, R, and SQL; experience with cloud\-based analytics platforms such as Databricks is preferred.
- Experience designing, developing, testing, and documenting complex systems, including predictive models, machine learning algorithms, and decision\-support tools.
- Demonstrated ability to work with large datasets and databases; familiarity with big data and data management technologies is a plus.
- Ability to solve ambiguous or open\-ended problems by creating structure and delivering actionable insights.
- Strong consulting and project leadership skills with the ability to influence and collaborate across functions.
- Ability to build productive relationships and work in a fast\-paced, competitive environment while handling multiple assignments and conflicting priorities
- Ability to manage multiple priorities in a fast\-paced environment with consistent, on\-time delivery.
- Continuous learner with an interest in emerging analytics, AI\-assisted workflows, and evolving applied meteorology practices.
Experience in the electric utility industry, particularly weather\-driven impacts to power generation or commercial optimization, is a plus.
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TRAVEL REQUIREMENTS
This position requires very little travel.
About Southern Company
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% 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 $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Southern Company AI Hiring
Southern Company has 2 open AI roles right now. They're hiring across AI Architect, Data Scientist. Positions span Atlanta, GA, US, Birmingham, AL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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