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About This Role
Are you looking for an exciting job where you can put your skills and talents to work at a company you can feel proud to be a part of? Do you want a workplace that will challenge you and offer you opportunities to learn and grow? A position at Xcel Energy could be just what you’re looking for.
Position Summary
A Data Scientist will focus on developing specialized knowledge in particular domains to effectively address business specific challenges. Will work on solving mid\-level complexity business problems by designing and implementing customized data solutions. This involves leveraging statistical analysis, machine learning techniques, and data visualization tools to create models and algorithms that provide actionable insights. By collaborating with other team members and stakeholders, they ensure that the solutions are aligned with business objectives and can be integrated into existing processes. Additionally, they will continuously refine their skills and stay updated with the latest advancements in data science to enhance their problem\-solving capabilities and contribute to the organization’s overall data strategy.
Essential Responsibilities
- Collecting and cleaning data from various sources and ensuring data quality.
- Manipulating and transforming data using tools like SQL, Python, and R.
- Use statistical techniques and machine learning algorithms to perform exploratory data analysis to understand the nuances, patterns, and anomalies within the data.
- Support development, validation, and deployment of machine learning \& statistical models to predict, classify, and optimize various business outcomes.
- Develop an understanding of delivering data science models in a production environment.
- Creating impactful visualizations to communicate findings effectively.
- Collaborating with stakeholders to understand business challenges and develop data\-driven solutions to real\-world problems.
- Translating technical concepts into actionable insights.
- Balancing learning with practical application.
- Understanding project and team scope as it relates to broader strategic initiatives.
- Effectively communicating findings to both technical and non\-technical audiences.
\*\* THIS POSITION IS ABLE TO BE HIRED AT EITHER OF THE LEVELS BELOW \*\*
Associate Data Scientist \- ($84,900 to $120,566\.66\)
Minimum Requirements
- Bachelor’s degree in computer science, data science, statistics, math or a related analytical concentration is required. Other bachelor’s degrees with data science relevant certifications will be considered.
Data Scientist \- ($97,600 to $138,600\)
Minimum Requirements
- Bachelor’s degree in computer science, data science, math, statistics, math, or related with an analytical concentration.
- A minimum of 3 years working as a data scientist or application developer (2 years with MS or Ph.D). Relevant certifications can be considered in lieu of a post graduate degree in combination with years of experience. (i.e. MLOps, data platform certs, etc.)
As a leading combination electricity and natural gas energy company, Xcel Energy offers a comprehensive portfolio of energy\-related products and services to 3\.4 million electricity and 1\.9 million natural gas customers across eight Western and Midwestern states. At Xcel Energy, we strive to be the preferred and trusted provider of the energy our customers need. If you’re ready to be a part of something big, we invite you to join our team.
All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Individuals with a disability who need an accommodation to apply please contact us at [email protected].
Non\-Bargaining
The anticipated starting base pay for this position is: $84,900\.00 to $138,600\.00 per year
This position is eligible for the following benefits: Annual Incentive Program, Medical/Pharmacy Plan, Dental, Vision, Life Insurance, Dependent Care Reimbursement Account, Health Care Reimbursement Account, Health Savings Account (HSA) (if enrolled in eligible health plan), Limited\-Purpose FSA (if enrolled in eligible health plan and HSA), Transportation Reimbursement Account, Short\-term disability (STD), Long\-term disability (LTD), Employee Assistance Program (EAP), Fitness Center Reimbursement (if enrolled in eligible health plan), Tuition reimbursement, Transit programs, Employee recognition program, Pension, 401(k) plan, Paid time off (PTO), Holidays, Volunteer Paid Time Off (VPTO), Parental Leave
Benefit plans are subject to change and Xcel Energy has the right to end, suspend, or amend any of its plans, at any time, in whole or in part.
In any materials you submit, you may redact or remove age\-identifying information including but not limited to dates of school attendance and graduation. You will not be penalized for redacting or removing this information.
Deadline to Apply: 08/09/26
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All Xcel Energy employees and contractors share responsibility for protecting the company's information and systems by adhering to cybersecurity policies, standards, and best practices, recognizing that cybersecurity is everyone's responsibility.
ACCESSIBILITY STATEMENT
Xcel Energy endeavors to make https://www.xcelenergy.com/ accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact Xcel Energy Talent Acquisition at [email protected]. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $84K-$138K range is in the lower quartile for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Xcel 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 789 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000. This role's midpoint ($111K) sits 42% below the category median. Disclosed range: $84K to $138K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Xcel Energy AI Hiring
Xcel Energy has 1 open AI role right now. They're hiring across Data Scientist. Based in Denver, CO, US. Compensation range: $138K - $138K.
Location Context
AI roles in Denver pay a median of $199,950 across 66 tracked positions. That's 7% below the national 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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