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About This Role
Job Specifications
Black Hills Energy is people powered and purpose driven. Our team uses the power of energy to improve life for over one million customers in 800\+ communities across the West and Midwest. We seek talented, caring people who embody our core values and contribute to a culture of inclusion and growth. As an organization, we believe the best part of working on our team is our commitment to making tomorrow better than today—for our customers, communities and each other.
Position summary:
The Data Scientist II will be engaged in statistical forecasting, mathematical optimization, machine learning, and artificial intelligence projects. This position will collaborate with cross\-functional teams to develop and deploy scalable AI\- and technology\-enabled solutions that enhance productivity, decision\-making, and business outcomes. The Data Scientist II will be dedicated to increasing technical proficiency, staying apprised of the latest industry developments, and evaluating innovative technologies that can deliver measurable value to the organization.
Pay Range: This posting includes the full pay range for this position. Pay is based on a number of factors and may vary depending on job\-related knowledge, skills, experience, and internal equity.
Level II: $79,400 \- $119,100
Senior: $97,150 \- $160,150
Principal: $116,500 \- $192,400
Reporting Relationship: Manager, Enterprise Data Science
Location: Our Company Headquarters in Rapid City, South Dakota.
Relocation Assistance: Relocation assistance is available based on individual circumstances! Details to be shared during the offer process.
Essential Functions:
- Builds robust computer programs to enable repeatable and efficient analysis using R and/or Python.
- Leverages machine learning, advanced statistics, and artificial intelligence to build predictive models, develop forecasts, and quantify complex relationships within data.
- Leverages large language models and generative AI tools
- Builds data pipelines and database objects within a SQL Server database using data engineering best practices.
- Uses SQL to efficiently query and update data.
- Strong professional communication skills, including the ability to explain model design, risks, and outputs, and the ability to collaborate within the team to create optimal products.
- Knowledge of best\-practice model risk management: validation, testing, and control standards, including testing of assumptions, computational accuracy, and documentation of model weaknesses and risks.
Additional Responsibilities:
- Continuous pursuit of innovative knowledge and skills in the fields of statistics, economics, operations research, computer science, and data science.
What Is Required:
Level II:
- Bachelor's Degree Data Science, Statistics, Computer Science, Applied Mathematics, related field or an equivalent combination of education and experience.
- Minimum of 3 years of data science experience.
Senior:
- Master's Degree Data Science, Statistics, Computer Science, Applied Mathematics, related field or an equivalent combination of education and experience.
- Minimum of 5 years of data science experience.
Principal:
- Master's Degree Data Science, Statistics, Computer Science, Applied Mathematics, related field or an equivalent combination of education and experience.
- Minimum of 8 years of data science experience.
What Is Desired:
- Experience prototyping and developing applications in R and/or Python.
- Experience developing projects using machine learning, advanced statistics, and artificial intelligence (AI).
- Experience using large language models and generative AI tools.
- Experience¿utilizing data engineering concepts and techniques.
- Experience using SQL to query and update data and build database objects.
*This description is not intended to be an all\-inclusive list of responsibilities, duties, and requirements for employees in this position. Job descriptions may and do change periodically. Where positions are covered by a collective bargaining unit agreement, the terms and conditions of the agreement will apply.*
About our Company: We are a customer, growth and safety focused utility company that is dedicated to our communities. We improve life with energy as an energy partner of choice. Our diverse culture fuels unique perspectives, opening doors to new insights and possibilities. Based in Rapid City, South Dakota, we have over 3000 employees and serve 1\.3 million natural gas and electric utility customers across eight states (South Dakota, Montana, Wyoming, Colorado, Nebraska, Iowa, Kansas, and Arkansas).
Enjoy our Comprehensive Benefits Package! Annual discretionary bonuses, 401(k) (6% company match and up to 9% company retirement contribution), tuition reimbursement, generous paid time off benefits, including paid holidays and parental leave, company paid life insurance and disability benefits (short and long term), critical illness, accident \& group hospital insurance, pet insurance, an employee assistance program and well\-being benefits, and competitive medical, dental and vision insurance.
Candidates must successfully pass a pre\-employment drug screen and background check. If there is anything that may show up in these reports that may conflict with the position requirements, feel free to contact the Black Hills Energy recruiting team at [email protected].
*Black Hills Energy does not sponsor applicants for work visas. All applicants must be legally authorized to work in the US.*
*We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or status as a protected veteran. If you require reasonable accommodation, please visit* *careers.blackhillsenergy.com* *for more information.*
Salary Context
This $79K-$192K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Black Hills Corporation, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($135K) sits 30% below the category median. Disclosed range: $79K to $192K.
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.
Black Hills Corporation AI Hiring
Black Hills Corporation has 1 open AI role right now. They're hiring across Data Scientist. Based in Golden, CO, US. Compensation range: $192K - $192K.
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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