Interested in this Data Scientist role at Constellation Energy?
Apply Now →Skills & Technologies
About This Role
Overview:
WHO WE ARE
As the largest private\-sector power producer in the world and the nation's largest producer of clean and reliable energy, Constellation is focused on our purpose: lighting the way to a brilliant tomorrow for all. We have been the leader in clean energy production for more than a decade, and we are cultivating a workplace where our employees can grow, thrive, and contribute. Now integrated with Calpine, our portfolio includes 55 gigawatts of capacity from nuclear, natural gas, geothermal, hydro, wind and solar facilities, with the generating capacity to power the equivalent of 27 million homes.
Our culture and employee experience make it clear: We are powered by passion and purpose. Together, we're creating healthier communities and a cleaner planet, and our people are the driving force behind our success. At Constellation, you can build a fulfilling career with opportunities to learn, grow and make an impact. By doing our best work and meeting new challenges, we can accomplish great things. Join us in meeting the country's energy needs today and tomorrow.
TOTAL REWARDS
Constellation offers an extensive selection of benefits and rewards to help our employees thrive professionally and personally. We provide competitive compensation and a wide\-range of benefits that support both employees and their families, helping them prepare for the future. In addition to highly competitive salaries, eligible employees are offered a bonus program, 401(k) with company match, employee stock purchase program; comprehensive medical, dental and vision benefits, including robust wellbeing programs; disability and life insurance benefits; paid time off for vacation, holidays, and sick days; and much more.
Expected salary range of $130,500 to $145,000, varies based on experience, along with comprehensive benefits package that includes bonus and 401(k).
Responsibilities:
PRIMARY PURPOSE OF POSITION
Apply the appropriate data science or analytical methods to extract knowledge and insights from data, which may take the form of time\-series (power plant equipment data, environmental data or other), structured (relational data stores), and unstructured (text and multi\-media) data sets. Closely collaborate with various internal stakeholders, information architects, data engineers, project/program managers, and other teams to turn data into critical information to inform decision making. This requires understanding business needs, providing and receiving regular feedback, and planning the proper transfer of developed solutions. Mine big and small data for insights, using advanced statistic and machine learning methods. Validate findings with the business by sharing analysis outputs in a way that can be understood by business stakeholders. Fill role of a subject matter expert in the areas of artificial intelligence, machine learning, feature engineering, data mining, and data manipulation/storage. Demonstrate commitment to continuous learning and professional development in technical subject matter. Share knowledge with team members, and business stakeholders, and IT partners. Collect, cleanse, standardize and analyze data from a variety of internal and external sources. Produce novel insights to help inform business actions using statistical modeling and machine learning techniques on complex data\-sets on the order of several terabytes or petabytes.
PRIMARY DUTIES AND ACCOUNTABILITIES
- Develop key predictive models that lead to delivering reduced overall annual expense for nuclear, performance improvement, and optimize specific performance criteria. Develop and recommend data sampling techniques, data collections, and data cleaning specifications and approaches. Apply missing data treatments as needed.
- Analyze data using advanced analytics techniques in support of process improvement efforts using modern analytics frameworks, including but not limited to Python, R, Scala, or equivalent; Spark, Hadoop file system and others
- Access and analyze data sourced from various Company systems of record. Support the development of strategic business and program implementation plans.
- Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high\-performance computing systems.
- Provide expert data and analytics support to multiple business units
- Works with stakeholders and subject matter experts to understand business needs, goals and objectives. Work closely with business, engineering, and technology teams to develop solution to data\-intensive business problems and translates them into data science projects. Collaborate with other analytic teams across Exelon on big data analytics techniques and tools to improve analytical capabilities.
MINIMUM QUALIFICATIONS
- Education: Bachelor's degree in a Quantitative discipline. Ex: Data Science, Data Analytics, Applied Mathematics, Statistics, Computer Science, Operations Research, or related field
- Experience: Between 5\-8 years of relevant experience developing hypotheses, applying machine learning algorithms, validating results to analyze large datasets and extract actionable insights is required. Previous research or professional experience applying advanced analytic techniques to large, complex datasets.
- Analytical Abilities: Strong knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization.
- Technical Knowledge: Proven experience in developing and deploying predictive analytics projects using one or more leading languages (Python, R, Scala, etc.).
- Communication Skills: Ability to translate data analysis and findings into coherent conclusions and actionable recommendations to business partners, practice leaders, and executives. Strong oral and written communication skills.
Qualifications:
PREFERRED QUALIFICATIONS* Education:
+ Masters, or PhD in a Quantitative discipline.
- Experience:
+ Strong Python proficiency with scikit\-learn, pandas, numpy
+ 3\+ years experience building and shipping real\-world business applications of classification/regression models in production
+ Expertise in feature engineering and model evaluation and tuning
+ Experience with MLOps tools (MLFlow or Azure ML)for model tracking and deployment
+ Ability to communicate technical concepts clearly to non\-technical stakeholders
+ Familiarity with time series or volatility analysis , clustering, decision tree learning, artificial neural networks etc.
+ Experience with imbalanced classification problems
+ Nice to have \- Domain knowledge in energy, finance, or commodity pricing
Salary Context
This $116K-$174K range is below the median for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Constellation 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($145K) sits 27% below the category median. Disclosed range: $116K to $174K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Constellation Energy AI Hiring
Constellation Energy has 1 open AI role right now. They're hiring across Data Scientist. Based in Hurstbourne Acres, KY, US. Compensation range: $174K - $174K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.