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About This Role
Who We Are: We're powering a cleaner, brighter future.
Exelon is leading the energy transformation, and we're calling all problem solvers, innovators, community builders and change makers. Work with us to deliver solutions that make our diverse cities and communities stronger, healthier and more resilient.
We're powered by purpose\-driven people like you who believe in being inclusive and creative, and value safety, innovation, integrity and community service. We are a Fortune 200 company, 20,000 colleagues strong serving more than 10\.7 million customers at six energy companies \- Atlantic City Electric (ACE), Baltimore Gas and Electric (BGE), Commonwealth Edison (ComEd), Delmarva Power \& Light (DPL), PECO Energy Company (PECO), and Potomac Electric Power Company (Pepco).
We're committed to creating an environment where every person can thrive. Our employee experience is grounded in four tenets that guide how we support our people: purposeful careers, growth opportunities, community impact, and support to thrive.
In our relentless pursuit of excellence, we elevate diverse voices, fresh perspectives and bold thinking. And since we know transforming the future of energy is hard work, we provide competitive compensation, incentives, excellent benefits and the opportunity to build a rewarding career.
Are you in? Primary Purpose: PRIMARY PURPOSE OF POSITION
Apply the scientific method to extract knowledge and insights from data, which may take the form of time\-series (smart\-meters, smart\-grid, and other IoT), structured (relational data stores), and unstructured (text and multi\-media) data sets. Train state of the art algorithmic models, including but not limited to tree\-based approaches and neural networks, and implement those models into a production environment following the established MLOps approaches. Closely collaborate with various internal stakeholders, information architects, data engineers, project/program managers, and other teams to turn data into analytics\-driven products and inform decision making. This requires understanding business needs, providing and receiving regular feedback, and planning the proper transfer of developed solutions. Validate findings with the business by sharing analysis outputs in a way that can be understood by business stakeholders. Become a subject matter expert in the areas of artificial intelligence, machine learning, feature engineering, and high\-performance computing. Demonstrate commitment to continuous learning and professional development in technical subject matter. Share knowledge with team members, and business stakeholders, and IT partners. A successful candidate will quickly adopt the team's established working processes and toolkit while growing his/her knowledge of the utilities industry. Position may be required to work extended hours for coverage during storms or other energy delivery emergencies.
Primary Duties: PRIMARY DUTIES AND ACCOUNTABILITIES* Develop key predictive models that lead to delivering a premier customer experience, operating performance improvement, and increased safety best practices. (25%)
- 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 (20%)
- Access and analyze data sourced from various Company systems of record. Support the development of strategic business, marketing, and program implementation plans. (20%)
- Provide expert data and analytics support to multiple business units (20%)
- Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high\-performance computing systems. (15%)
Job Scope: JOB SCOPE
Support business unit strategic planning while providing a strategic view on machine learning technologies. Advice and counsel key stakeholders on machine learning findings and recommend courses of action that redirect resources to improve operational performance or assist with overall emerging business issues. Provide key stakeholders with machine learning analyses that best positions the company going forward. Educate key stakeholders on the organizations advance analytics capabilities through internal presentations, training workshops, and publications.
Minimum Qualifications: MINIMUM QUALIFICATIONS* Bachelor's or Master's degree from a leading program in a Quantitative discipline. Ex: Applied Mathematics, Computer Science, Finance, Operations Research, Physics, Statistics, or related field
- Intern experience in a data science position or previous research or professional experience applying advanced analytic techniques to large, complex datasets.
- Strong knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization.
- Demonstratable experience in your analytics/statistics/visualization platform of choice, but preferably in the MS Azure suite as well as Python, SQL. using big data technologies like Spark, Dask, etc.
- 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.
Preferred Qualifications: PREFERRED QUALIFICATIONS* Master's from a leading program in a Quantitative discipline
- Prior exposure to data structures pertaining to smart\-meters, billing, or outage management systems. Prior exposure to the utilities or broader energy sector.
- Solid understanding of relevant theories in machine learning, statistics, probability theory, data structures and algorithms, optimization, etc.
- Expert level coding skills (Python, R, Scala, etc), and experience developing in a Unix environment.
- Ability to translate executive and analytics leaders vision and guidance into methods and analytics. Strong time management and presentation skills. Experience presenting to diverse audiences including presenting to conferences and business symposia.
Benefits:
- Annual salary will vary based on a candidate’s skills, qualifications, experience, and other factors: $59,200\.00/Yr. – $81,400\.00/Yr.
- Annual Bonus for eligible positions: 7%
- 401(k) match and annual company contribution
- Medical, dental and vision insurance
- Life and disability insurance
- Generous paid time off options, including vacation, sick time, floating and fixed holidays, maternity leave and bonding/primary caregiver leave or parental leave
- Employee Assistance Program and resources for mental and emotional support
- Wellbeing programs such as tuition reimbursement, adoption and surrogacy assistance and fitness reimbursement
- Referral bonus program
- And much more
Note: Exelon\-sponsored compensation and benefit programs may vary or not apply based on length of service, job grade, job classification or represented status. Eligibility will be determined by the written plan or program documents.
Salary Context
This $59K-$88K range is in the lower quartile 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 ComEd, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($74K) sits 63% below the category median. Disclosed range: $59K to $88K.
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.
ComEd AI Hiring
ComEd has 1 open AI role right now. They're hiring across Data Scientist. Based in Oakbrook Terrace, IL, US. Compensation range: $88K - $88K.
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
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