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About This Role
The Data Scientist II leverages machine learning and Generative AI (LLMs) to deliver scalable, data\-driven solutions that improve business performance and decision\-making. This role builds and deploys predictive and LLM\-based models using modern tools (CI/CD, Airflow), develops impactful insights through strong analytics and Power BI visualizations, and partners with stakeholders to identify high\-value opportunities. The ideal candidate has strong analytical skills, a keen eye for data, and a passion for applying AI to real\-world problems.
Key Objectives:
- Deliver measurable business impact using machine learning and GenAI/LLM\-driven solutions.
- Improve operational performance through scalable, production\-ready analytics.
- Develop and maintain a suite of Power BI reports and dashboards to enable informed, data\-driven business decisions.
- Enable smarter decision\-making through data storytelling and visualization.
- Identify and implement high\-value GenAI use cases across the organization.
- Promote responsible and effective use of AI and advanced analytics.
- Mentor junior team members and help elevate overall team capabilities.
Essential Duties \& Responsibilities:
- Analyze and integrate large, complex datasets from multiple sources, with cloud environments preferred.
- Design, build, and deploy machine learning models and LLM\-powered solutions.
- Develop GenAI use cases such as text classification, embeddings, summarization, and decision\-support tools.
- Productionize models using CI/CD pipelines and orchestrate workflows using Airflow DAGs.
- Monitor model performance, maintain documentation, and support governance, reliability, and ongoing model maintenance.
- Translate analytical findings into clear and actionable business insights for technical and non\-technical audiences.
- Build dashboards and visualizations using Power BI to track KPIs, trends, and model outcomes.
- Partner with stakeholders to identify opportunities for advanced analytics and AI adoption.
- Apply strong data validation and quality checks to ensure data accuracy, completeness, and integrity.
- Support ethical AI practices, data privacy requirements, and governance standards.
- Mentor junior data scientists and contribute to team best practices and standards.
Required Skills:
- Strong proficiency in SQL and Python, or R.
- Hands\-on experience developing and deploying machine learning models.
- Experience with Generative AI and LLMs, including prompting, embeddings, and NLP\-related use cases.
- Experience with CI/CD pipelines, Airflow, and workflow orchestration.
- Strong experience with Power BI or similar data visualization tools.
- Excellent analytical and problem\-solving skills with strong attention to detail.
- Strong data intuition and the ability to identify patterns, anomalies, and meaningful insights.
- Ability to communicate complex analytical and AI concepts clearly to technical and non\-technical audiences.
- Experience with version control tools such as Git and collaborative development practices.
- Ability to work independently and effectively in ambiguous environments.
Preferred Qualifications:
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience operationalizing LLM\-based solutions in production environments.
- Familiarity with MLOps and model lifecycle management.
- Demonstrated passion for AI innovation and continuous learning.
Work Experience:
- 3\+ years of expe rience in data science, advanced analytics, or a related field.
- Proven experience building and deploying machine learning solutions in production.
- Experience applying statistical analysis and predictive modeling.
- Exposure to or hands\-on experience with GenAI and LLM applications is strongly preferred.
Education:
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field required.
Other:
- Must be able to travel occasionally should a business need arise. For most roles travel would not be common. Travel may involve plane, car or metro. In accordance with ADA policies, reasonable accommodations regarding travel limitations can be provided. Travel will be more common for roles such as Account Executives (25 \- 50%), senior leaders (10 – 20%) or Capella Core Faculty (5 – 10%).
- Ability to work onsite in Corporate or Campus location (in a typical office environment) may be required based on role. If so, this would include being mobile within the office, including movement from floor\-to\-floor using elevators or stairs.
- If offsite or hybrid role, must have access to work in setting which enables meeting all requirements of the role (including privacy, reliable internet access, phone, ability to video conference, etc.) at a remote location.
- Faculty and Federal Work Study roles require access to work in setting which enables meeting all requirements of the role (including computer, privacy, reliable internet access, phone, ability to video conference, etc.) at a remote location.
- This role may require lifting, however reasonable accommodations will be provided in accordance with our ADA policies.
- Must be able to meet critical thinking and problem solving aspects aligned to job duties, as well as effectively communicating with co\-workers.
- Must be able to work more than 40 hours per week when business needs warrant. Accommodations related to schedule may be considered.
- Able to access information using a computer.
Other essential functions and marginal job functions are subject to modification.
*
SEI offers a comprehensive package of benefits to employees scheduled 30 hours or more per week. In addition to medical, dental, vision, life and disability plans, SEI employees may take advantage of well\-being incentives, parental leave, paid time off, certain paid holidays, tax saving accounts (FSA, HSA), 401(k) retirement benefit, Employee Stock Purchase Plan, tuition assistance as well as entertainment and retail discounts. Non\-exempt employees are eligible for overtime pay, if applicable.
Careers \- Our Benefits, Strategic Education, Inc
SEI is an equal opportunity employer committed to fostering an inclusive and collaborative culture where individuals can grow their careers and contribute fully. We strive to attract talent with broad experiences, skills and perspectives. We welcome applications from all. While it is not typical for an individual to be hired at or near the top end of the pay range at SEI, we offer a competitive salary. The actual base pay offered to the successful candidate may vary depending on multiple factors including, but not limited to, job\-related knowledge/skills, experience, business needs, geographical location, and internal pay equity. Our Talent Acquisition Team is ready to discuss your interest in joining SEI. The expected salary range for this position is below.
$95,100\.00 \- $142,600\.00 \- Salary
*If you require reasonable accommodations to complete our application process, please contact our Human Resources Department at* *[email protected]* .
Salary Context
This $95K-$142K 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 Strategic Education, Inc., 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($118K) sits 38% below the category median. Disclosed range: $95K to $142K.
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.
Strategic Education, Inc. AI Hiring
Strategic Education, Inc. has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Minneapolis, MN, US. Compensation range: $142K - $159K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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