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About This Role
Position Overview: Power your future with Qualus as a Data Scientist at levels I, II or Senior. The Data Scientist is responsible for performing analyses involving Demand Side Management program evaluation and research for electric and gas utilities and other clients across North America. DSM programs include energy efficiency and demand response (load curtailment) programs and related projects. The scope of work for a Data Scientist generally requires some collaboration with other DSM Analysts, Data Scientists and/or Engineers, and may include team leader roles for candidates who have leadership capabilities and interests. This is Not a pure IT Data Scientist role. Responsibilities:
- Assist with planning, designing, and implementing research and sample design projects efforts to support program evaluations
- Conduct primary and secondary data collection and statistical and econometric data analysis
- Apply statistics and econometrics to analyze and interpret data to reach meaningful conclusions and recommendations for inclusion in clearly written, concise reports
- Optimize and automate existing data-flow processes including file imports/exports and data-refresh processes
- Produce data visualizations to distill information and provide meaningful conclusions and recommendations for industry clients
- Analyze collected data using analytical software, identifying trends, summarizing program events, and identifying causal impacts
- Follows data integrity, quality standards, frameworks and templates, naming conventions, coding standards, and adheres to best data management practices
- Effectively communicate research findings, conclusions, implications, and recommendations in presentations and meetings
- Proactively check own work (data, analysis, writing) for accuracy and make corrections as needed
Minimum Qualifications:
- Experience in statistical analysis, predictive modelling, simulation, and machine learning
- Experience with common data science tools, such as R, Python, MATLAB; with a heavy emphasis on R
- Experience analyzing large and complex data sets, with strong aptitude for conducting quantitative and qualitative analysis
- Visualization skills such as Tableau, Power BI, or R Shiny
- Master's degree in economics, statistics, mathematics, data science, or other quantitative science or social science
- Experience supporting research oriented data analysis
- 1-3 years of experience in data science or applied statistical analysis. Job level will be commensurate with experience and qualifications.
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Benefits & Compensation:
Qualus benefits offered include Medical, Dental, Vision, Life Insurance, Short and Long-Term Disability, 401(k) match, Flexible Spending Accounts, EAP, Parental Leave, Paid time off, and Holidays, for those who qualify.
The final salary awarded for this role may vary from the above range based on several factors including, but not limited to, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. The salary range provided in this job posting may be subject to change for business purposes. Company Overview:
Qualus is a leading pure-play power services firm and innovator at the forefront of the energy transition and transformation, with differentiated capabilities across grid modernization, resiliency, security, and sustainability. The firm partners with utilities, commercial, industrial, data center, and government clients, and renewable and energy storage developers, offering comprehensive solutions through boutique and integrated advisory, planning, engineering, digital solutions, program management, and specialized field services. Qualus also provides software and technology enabled services and develops breakthrough solutions for critical power industry challenges such as distributed and variable resource integration, emergency management, and secure data exchange. The firm has nearly 1,600 professionals, with offices throughout the U.S. and Canada. Equal Opportunity Employer:
At Qualus, we believe everyone has value; and that the diversity and inclusion among our teams is what sets us apart for optimal success. We put people first because we care. To view a copy of the Qualus Equal Opportunity and Affirmative Action Policy Statement, click here.
If you are an individual with a disability and require a reasonable accommodation to complete any part of the application process, or are limited in the ability or unable to access or use this online application process, and need an alternative method for applying, please email [email protected].
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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Qualus, 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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Qualus AI Hiring
Qualus has 1 open AI role right now. They're hiring across Data Scientist. Based in NV, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.
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