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About This Role
About Us: AAR Corp. (NYSE: AIR) is a global aerospace and defense aftermarket solutions company that employs more than 6,000 people across over 60 sites in over 20 countries. Headquartered in the Chicago, Illinois area, AAR supports commercial and government customers in more than 100 countries through four operating segments: Parts Supply, Integrated Solutions, Repair and Engineering and Expeditionary Services. AAR’s purpose is to empower people to build innovative aerospace solutions today so you can safely reach your destination tomorrow. The company’s mission is to go above and beyond to provide value\-driven aerospace aftermarket solutions to meet the evolving needs of our customers worldwide. AAR constantly searches for the right thing to do for its customers, employees, partners and for society.
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Data Scientist \- 18483Description
The AAR Parts Supply Distribution Analytics Team are developing an AI\-driven aviation market intelligence tool designed to transform how aviation parts distribution businesses understand market share, identify growth opportunities, and make strategic decisions. This platform combines advanced analytics, machine learning, agentic AI, and intuitive user experience to provide real\-time insights into customers, parts, and markets.
The Data Scientist will define, build, and continuously improve the analytical and machine learning models that power the platforms core decision\-making capabilities. This role translates complex business problems into scalable, production\-ready models that drive market sizing, forecasting, and opportunity identification. The Data Scientist partners closely with product, engineering, and data teams to ensure models are accurate, explainable, and embedded into real\-world workflows. Success in this role requires strong technical depth, business intuition, and the ability to operate in ambiguous, data\-rich environments.
This position is based at our Corporate Headquarters in Wood Dale, IL, with a planned relocation to the Merchandise Mart (Chicago) in early 2027\.
What you will be responsible for:
- Design, develop, and own scalable analytical and machine learning models for market sizing, forecasting, opportunity identification, and optimization use cases.
- Translate ambiguous business problems into structured modeling approaches, including feature engineering, model selection, and evaluation frameworks.
- Design and analyze experiments, statistical tests, and validation methods to measure model quality and business impact.
- Deploy and integrate models into production systems in collaboration with data engineering and backend teams, ensuring reliability, scalability, and performance.
- Work with large, complex, and imperfect datasets; define data requirements and support robust preprocessing and feature pipelines.
- Ensure model outputs are explainable, interpretable, and aligned with business logic to support user trust and adoption.
- Monitor, validate, and improve model performance through testing, retraining, versioning, and feedback loops.
- Partners with product teams to define analytical features, influence roadmap decisions, and embed model\-driven insights into decision\-making workflows.
Qualifications What you need to be successful in this role:
- Strong foundation in statistics, machine learning, and predictive modeling, including regression, classification, clustering, time series, and experiment design e.g., AB testing.
- Proficiency in Python and SQL, with experience using common ML libraries such as scikit\-learn, pandas, and numpy.
- Experience building end\-to\-end ML workflows, including data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring.
- Familiarity with production ML practices, including model versioning, performance optimization, retraining, and monitoring for drift or degradation.
- Experience working with large\-scale or complex datasets, including handling missing data, inconsistencies, and real\-world data limitations.
- Ability to communicate model logic, assumptions, and outputs clearly to non\-technical stakeholders and cross\-functional partners.
- Experience collaborating with product managers, data engineers, and software engineers to deliver analytical features in production environments.
- Strong problem\-solving skills and the ability to operate effectively in ambiguous environments with evolving requirements.
- Bachelors in data science, Statistics, Mathematics, Engineering, Computer Science, or a related quantitative field.
- Master's degree preferred
- 3 to 5 plus years of experience in data science, machine learning, or advanced analytics roles in a product or business\-driven environment.
- Demonstrated experience developing, deploying, and improving predictive models that drive business decision\-making.
- Experience working on analytics or ML\-driven products such as forecasting, optimization, recommendation systems, or market intelligence tools.
- Experience in designing experiments or statistical validation approaches to evaluate model performance and business outcomes is preferred.
- Experience with cloud platforms or distributed data processing such as AWS, Azure, or Spark is preferred but not required.
- Experience in aviation, supply chain, or related industries is a plus but not required.
The rewards of your career at AAR go far beyond just your salary:
- Competitive salary and bonus package
- Comprehensive benefits package including medical, dental, and vision coverage.
- 401(k) retirement plan with company match
- Generous paid time off program
- Professional development and career advancement opportunities
Physical Demands/Work Environment:
The physical demands and work environment characteristics described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
- Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- While performing the duties of this job, the employee may be regularly required to sit, stand, bend, reach and move about the facility.
- The environmental characteristic for this position is an office setting.
- Candidates should be able to adapt to a traditional business environment.
AAR provides accommodation in accordance with applicable laws through all stages of the hiring process. If you require accommodation for any part of the application and/or hiring process, please advise Human Resources.Compensation:
The anticipated salary range for this position is $115,000 to $130,000 annually. This range reflects the base salary for candidates who meet the requirements of the role, including experience, education, and location. In addition to base pay, this role is eligible for a bonus. AAR offers a competitive benefits package, including medical/dental/vision/life/and AD\&D insurance, 401(k) savings plan with employer match, paid time off and holiday pay, as well as opportunities for professional development and growth.
\#LI\-MA1 \#LI\-ONSITE
Job: Information TechnologyPrimary Location: United States\-Illinois\-Wood DaleSchedule: Full\-timeOvertime Status: Exempt
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 or veteran status.
Salary Context
This $115K-$130K 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 AAR Corp., 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 ($122K) sits 36% below the category median. Disclosed range: $115K to $130K.
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.
AAR Corp. AI Hiring
AAR Corp. has 1 open AI role right now. They're hiring across Data Scientist. Based in Wood Dale, IL, US. Compensation range: $130K - $130K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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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