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Airlines Data Science Jobs (NOW HIRING)

American Airlines

Senior Engineer, IT Data

Fort Worth, TX · On-site

$101K - $138K/yr

Join our American Airlines family, and you'll travel the world, grow your expertise and become the ... The Data domain leans into Machine Learning and AI, as well as Data Science and Business ...

CIG COMPANIES SERVICES

Head of Data & AI

Orlando, FL · On-site

... airline operations, hospitality, financial services, and solar field construction. Key ... Build and manage a multidisciplinary team of data engineers, data scientists, and analytics ...

SITA Switzerland Sarl

Lead Data Scientist

Fallston, NC · On-site +1

Overview WELCOME TO SITA At SITA, we keep airports moving, airlines flying smoothly, and borders ... Master's degree in Data science, Computer Science, Mathematics, Physics or equivalent proven ...

United Airlines

Senior Data Scientist

Austin, TX · On-site

Proficient in Python, SQL, and data science libraries (scikit-learn, pandas, etc.) * Experience with cloud environments like Databricks and execution workflows * Familiarity with version control ...

Frontier Airlines

Analyst II - Ancillary Revenue

Denver, CO · On-site

$62K - $86K/yr

The Analyst II, Ancillary Revenue will develop and apply statistical modeling and data science as ... Experience in an airline pricing, yield- or revenue management, revenue analytics, revenue systems ...

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All JobsAirlines Data Science Jobs

Airlines Data Science information

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$46K

$165K

$243.5K

How much do airlines data science jobs pay per year?

As of Jun 7, 2026, the average yearly pay for airlines data science in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

What is the difference between Airlines Data Science vs Airlines Data Analysis?

AspectAirlines Data ScienceAirlines Data Analysis
Required CredentialsDegree in Data Science, Statistics, or related field; programming skillsDegree in Data Analysis, Business, or related field; analytical skills
Work EnvironmentDeveloping models, algorithms, predictive analyticsInterpreting data, generating reports, supporting decision-making
Employer & Industry UsageUsed in predictive modeling, machine learning projects within airlinesUsed for reporting, trend analysis, and operational insights in airlines

Airlines Data Science focuses on building models and algorithms to predict and optimize airline operations, requiring advanced technical skills. Airlines Data Analysis involves examining data to generate reports and insights to support business decisions. Both roles are essential in the airline industry but differ in technical complexity and scope.

What are Airlines Data Scientists?

Airlines Data Scientists are professionals who use data analysis, statistical modeling, and machine learning techniques to solve problems and improve operations within the airline industry. They analyze large datasets related to flight operations, customer behavior, revenue management, and scheduling to help airlines optimize routes, pricing, and customer experience. Their work enables airlines to make data-driven decisions, enhance efficiency, and remain competitive in a dynamic industry.

What are the key skills and qualifications needed to thrive as an Airlines Data Scientist, and why are they important?

To thrive as an Airlines Data Scientist, you need strong analytical skills, a background in statistics or computer science, and experience with data modeling and analysis, often supported by a relevant degree. Proficiency with tools like Python, R, SQL, and specialized airline data platforms, along with certifications in data science or analytics, is typically required. Excellent problem-solving, communication, and teamwork skills help you translate complex data insights into actionable strategies for diverse airline stakeholders. These skills are crucial for optimizing operations, improving customer experience, and driving data-driven decision-making in a highly competitive industry.

How do data scientists in the airline industry typically collaborate with other departments to improve operations or customer experience?

Airlines data scientists regularly work alongside departments such as revenue management, operations, marketing, and customer service to tackle complex business challenges. For example, they may partner with operations teams to optimize flight schedules or help marketing teams personalize promotions based on passenger data. This cross-functional collaboration usually involves translating data insights into actionable strategies, requiring strong communication skills and an understanding of airline-specific metrics. Such teamwork ensures that data-driven solutions are practical, scalable, and aligned with organizational goals.
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What states have the most Airlines Data Science jobs? States with the most job openings for Airlines Data Science jobs include:
What job categories do people searching Airlines Data Science jobs look for? The top searched job categories for Airlines Data Science jobs are:
Airlines Data Science jobs near you
Infographic showing various Airlines Data Science job openings in the United States as of May 2026, with employment types broken down into 5% Internship, 69% Full Time, 5% Part Time, and 21% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Staff Data Scientist- Pricing Science

Staff Data Scientist- Pricing Science

CSC Generation

Austin, TX • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted yesterday

Job description

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.
Reports to: Director of Finance and Business Intelligence
Location: Remote - US or Canada
About the Role
As our Staff Data Scientist, you will design and ship production pricing systems such as demand forecasting, price elasticity modeling, dynamic pricing and the experimentation infrastructure needed to measure whether they actually work.
This is a hard, high-stakes problem: your models will directly influence margin and revenue decisions across a portfolio of brands operating at scale. You will own the full arc from framing ambiguous business problems as well-defined ML tasks through to monitoring models that hold up in production.
At six months, success looks like at least one pricing model shipped to production with measurable business impact and an experimentation framework in place that your stakeholders trust. If you have spent time building pricing systems from the ground up, not just consuming them, and you care deeply about rigorous causal inference and honest model evaluation, this role was written for you.
What You'll Do
  • Design and build production ML systems for pricing, demand forecasting, and related revenue problems
  • Frame ambiguous business problems as well-defined ML tasks with clear success criteria and measurable outcomes
  • Set the standard for model evaluation, validation, and monitoring - including knowing when CV metrics are misleading and when holdout testing is the only honest answer
  • Build robust predictive models across classification, regression, time series, and causal inference
  • Identify and prevent data leakage, overfitting, and other failure modes before they reach production
  • Design and analyze experiments to measure causal impact of pricing decisions
  • Debug models that fail in production - understand why they fail, not just that they do
  • Translate model limitations, uncertainty, and risk clearly to both technical and non-technical stakeholders
  • Partner with product, engineering, and business teams to ensure ML solutions solve real problems

Required Qualifications
  • 7+ years of applied ML / data science experience with a track record of production systems that delivered measurable business impact.
  • Deep experience in pricing, demand forecasting, or revenue optimization - you have built these models end-to-end, not just consumed them.
  • Expert-level Python and SQL.
  • Deep understanding of ML fundamentals beyond API-level usage, including model evaluation, validation, and failure mode diagnosis.
  • Strong grounding in causal inference and experimental design, including the ability to distinguish correlation from causal result.
  • Ability to work with messy, real-world data and make pragmatic tradeoffs under ambiguity.
  • Familiarity with cloud ML platforms (GCP/Vertex AI or AWS/SageMaker).
  • MS or PhD in Statistics, Computer Science, Operations Research, or a related quantitative field.

Preferred Qualifications
  • Experience in e-commerce, retail, marketplace, or pricing-intensive industries such as airlines, ride-sharing, or fintech.

Why Join
The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
  • Portfolio-Level Impact: Your models will influence pricing and margin decisions across a $1B+ portfolio of brands - the output of your work is visible at the executive level from day one.
  • AI-First Skill Building: Get hands-on with production ML infrastructure, causal inference at scale, and the Genesis platform - building a modern, applied ML skill set on real retail data problems.
  • Ownership: You will own the full problem from framing through production, with the autonomy to make technical decisions and the stakeholder access to see them through.
  • Competitive Benefits (CAN): Comprehensive benefits including paid time off, RRSP match, group benefits, and employee discounts across portfolio brands.
  • Competitive Benefits (US): Comprehensive benefits including paid time off, 401(k) match, medical, dental, vision, supplemental coverage, and employee discounts across portfolio brands.

Interview Process
  1. Recruiter Screen: 30-minute call to cover your background, the role, and logistics.
  2. Hiring Manager Interview: Conversation with the Director of Finance and Business Intelligence focused on your pricing science experience, approach to ambiguous ML problems, and how you've driven production impact.
  3. Technical / Case Discussion: Deep dive into a pricing or demand forecasting problem - expect questions on model evaluation, causal inference, and production failure modes. Cross-functional stakeholders may join.
  4. Executive Interview: Final conversation with senior leadership.
  5. Reference Checks: Conducted in parallel with the final stages where possible.
  6. Offer: We move quickly for the right candidate.

For US-based candidates, this posting is intended for candidates that reside in the following states:
AZ, DE, FL, GA, IN, LA, MI, MS, MO, NV, NC, OK, PA, TN, TX, UT, WV, WI, and WY.
For Ontario applicants, please note that this posting is for an existing vacancy.
The CSC Generation family of brands provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, provincial, state or local laws.
The CSC Generation family of brands is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need assistance or accommodation due to a disability, please contact [email protected].
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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About CSC Generation

Sourced by ZipRecruiter

CSC Generation is a multi-brand technology platform based in Merrillville, IN, United States. The organization operates in the retail sector and utilizes technology to save retail companies from going into bankruptcy, while also offering consumers the ability to lease their purchases. Founded by serial entrepreneur, Justin Yoshimura, CSC Generation has leveraged its proprietary technology and customer database to quickly revitalize distressed retail brands. The company's mission revolves around the concepts of reinvention and innovation as it aims to redefine traditional retail and direct-to-consumer models in today's digital age. Notably, the company has, to date, acquired several brands such as DirectBuy, Killion, and most notably, Z Gallerie, growing fast within the e-commerce sector.

Company size

501 - 1,000 Employees

Headquarters location

Merrillville, IN, US

Year founded

2016

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