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About This Role
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Date: Jul 8, 2026
Location(s): Juno Beach, FL, US, 33408
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Company: NextEra Energy
Requisition ID: 95280
NextEra Energy Resources is one of America’s largest wholesale electricity generators, harnessing diverse energy sources to power progress. We deliver tailored energy solutions that fuel economic growth, strengthen communities, and help customers achieve their energy goals. Ready to make a lasting impact? Take the next step in your career with us!
Position Specific Description
We are seeking a Data Scientist with strong expertise in Machine Learning (ML) and Generative AI (LLMs) to design, develop, and deploy intelligent, data\-driven solutions within a modern cloud environment.
This role involves close collaboration with data engineers and business stakeholders to build and operationalize production\-grade AI systems using AWS and cutting\-edge LLM frameworks.
Core Responsibilities
Design, develop, and deploy machine learning and generative AI models to address complex business challenges and enhance decision\-making.
Implement Retrieval\-Augmented Generation (RAG) and semantic search pipelines that integrate large language models with enterprise data sources.
Apply prompt engineering, embedding techniques, and model fine\-tuning using frameworks such as LangChain, LlamaIndex, and Hugging Face Transformers.
Collaborate with engineering teams to ensure scalable data ingestion, ETL/ELT workflows, and model deployment across cloud environments.
Communicate technical insights and analytical results clearly to both technical and non\-technical audiences.
Contribute to Agile development processes and maintain code through Git/GitHub, CI/CD automation, and infrastructure\-as\-code (CloudFormation or equivalent).
Preferred Qualifications
2\+ years of hands\-on experience with Large Language Models (LLMs) or Generative AI, such as GPT, Claude, Llama, or Mistral.
Proficiency in Python and key libraries including pandas, NumPy, scikit\-learn, PyTorch, and TensorFlow.
Experience implementing RAG architectures, vector databases (e.g., Pinecone, FAISS, Weaviate), or LLM orchestration frameworks (e.g., LangChain, Semantic Kernel, CrewAI, LangGraph).
Practical experience working with AWS services (SageMaker, Lambda, S3, RDS, etc.).
Strong foundation in statistics, linear algebra, and optimization for model design and evaluation.
Experience creating insightful data visualizations using Tableau, Power BI, Plotly, or matplotlib.
Familiarity with big data technologies such as Spark, Databricks, or Snowflake.
Deep understanding of machine learning algorithms, data preprocessing, and model evaluation techniques.
Strong problem\-solving abilities with a focus on reproducibility, scalability, and model governance.
Excellent communication skills, with the ability to translate complex analytical findings into actionable business insights.
Demonstrated curiosity and enthusiasm for staying current with advances in AI, Generative AI, and ML infrastructure.
Proven ability to work effectively in Agile and cross\-functional team environments.
Nice to Have
Experience with multi\-agent orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen).
2\+ years of experience in applied data science or machine learning.
At least 1 year of direct, hands\-on experience developing or deploying LLMs or Generative AI systems.
Demonstrated experience with working with MLOps engineers to deploying, monitoring, and maintaining production ML models using cloud platforms.
Knowledge of energy market data, time\-series forecasting, or optimization modeling.
Familiarity with reinforcement learning, probabilistic modeling, or stochastic optimization techniques.
Job Overview
This position is responsible for developing algorithms, modeling techniques, and optimization methods that support many aspects of NextEra and FPL business. Employees in this role use knowledge of machine learning, optimization, statistics, and applied mathematics along with abilities in software engineering with a focus on distributed computing and data storage infrastructure (e.g. “Big Data”).
Job Duties \& Responsibilities
Develops machine learning, optimization and other modeling solutions
Prepares comprehensive documented observations, analyses and interpretations of results including technical reports, summaries, protocols and quantitative analyses
Works with a variety of datasets, including timeseries data and “big data” requiring analysis on distributed computing platforms
Writes software in R, Python or similar languages and contributes code to product development teams
Performs other job\-related duties as assigned
Required Qualifications
Bachelor’s Degree
Experience: 2\+ years
Preferred Qualifications
Master’s Degree
Doctoral Degree
NextEra Energy offers a wide range of benefits to support our employees and their eligible family members.
Employee Group: Exempt
Employee Type: Full Time
Job Category: Science, Research, and Technology
Organization: NextEra Energy Resources, LLC
Relocation Provided: No
NextEra Energy is an Equal Opportunity Employer. Qualified applicants are considered for employment without regard to race, color, age, national origin, religion, marital status, sex, sexual orientation, gender identity, gender expression, genetics, disability, protected veteran status or any other basis prohibited by law.
NextEra Energy and provides reasonable accommodation in its application and selection process for qualified individuals, including accommodations related to compliance with conditional job offer requirements, consistent with federal, state, and local laws. Supporting medical or religious documentation will be required where applicable and permitted by applicable law. To request a reasonable accommodation, please send an e\-mail to recruiting\[email protected], providing your name, telephone number and the best time for us to reach you.
NextEra Energy will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information.
NextEra Energy does not accept any unsolicited resumes or referrals from any third\-party recruiting firms or agencies.
Nearest Major Market: Palm Beach
Nearest Secondary Market: Miami
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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 NextEra Energy, 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.
NextEra Energy AI Hiring
NextEra Energy has 1 open AI role right now. They're hiring across Data Scientist. Based in Juno Beach, FL, 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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