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About This Role
Data Scientist Specialist \- Customer Service
Textron Aviation has been inspiring the journey of flight for nine decades through the iconic and beloved Cessna and Beechcraft brands. We are passionate advocates of aviation, empowering people with the freedom of flight. As you join our legacy as a global leader in private aviation, you’ll have opportunities to try new fields, expand your skills and knowledge, stretch your abilities, and build your career. We provide a competitive and extensive total rewards package that includes pay and innovative benefits to support you and your family members – now and in the future, beginning day one. Your success is our success.
Description
JOB SUMMARY:
Data Scientists drive strategic initiatives by performing advanced analytics, predictive modeling, and machine learning on complex internal and external datasets. They leverage advanced mathematics, statistics, and code\-first tools to deliver high\-impact, actionable insights. Unlike Data Analysts, Data Scientists focus on building and validating models, conducting experiments, and generating recommendations that directly inform business strategy and operational transformation.
We are seeking an experienced Data Scientist to join our small, specialized, and highly motivated team that is implementing predictive maintenance and connected aircraft solutions for a large fleet of Textron Aviation aircraft. In this senior position, you will merge large scale time\-series aircraft sensor data, parts, and maintenance records using a variety of machine learning, deep learning, AI, and visualization methods. You will keenly focus on maximizing the operational availability of customer jet, turboprop, and piston aircraft along with efficient aircraft maintenance practices. As a key part of your role, you will mentor and provide technical leadership to junior data scientists, foster creativity, and champion innovative, cutting\-edge data science methodologies across the organization.
At Textron Aviation, we are building a community of Data \& Analytics professionals with an emphasis on collaboration and cross functional support. You will have the opportunity to work closely with your peers throughout the organization toward a vision of data driven strategy.
JOB RESPONSIBILITIES:
- Support data exploration, hypothesis testing, and foundational predictive analytics.
- Independently develop machine learning models for classification, regression, clustering, and time\-series analysis using Python and relevant libraries.
- Collaborate with analysts and engineers to prepare and clean data for modeling.
- Document methodologies and contribute to reproducible research practices.
- Develop foundational understanding of the business domain, including aircraft OEM operations.
- Apply advanced statistical techniques to evaluate model performance and business impact.
- Work with large datasets using Python, SQL, and cloud\-native platforms.
- Communicate findings through technical documentation and stakeholder presentations.
- Contribute to the development of analytics best practices and model governance.
- Lead development of predictive models and advanced analytics workflows.
- Mentor junior data scientists and contribute to team knowledge sharing.
- Design experiments and causal inference studies to support strategic initiatives.
- Collaborate with cross\-functional teams to integrate models into business processes.
- Champion adherence to governance frameworks and global data science standards.
- Drive innovation in data science methodologies and tools.
- Influence enterprise\-wide strategy through advanced modeling and simulation.
- Lead cross\-domain projects involving deep learning, NLP, or advanced time\-series forecasting.
- Advocate for ethical AI practices and model interpretability.
- Serve as a thought leader in data science governance, ensuring consistency and quality across teams.
Qualifications
EDUCATION/ EXPERIENCE:
- Bachelor’s degree required in Applied Mathematics, Statistics, Computer Science, Data Science, or related technical field/coursework.
- Minimum 7 years of relevant experience required in data science, machine learning, artificial intelligence, predictive modeling, or advanced analytics.
- Master’s or PhD degree in Applied Mathematics, Statistics, Computer Science, Data Science, or related field preferred.
- Aviation experience preferred.
QUALIFICATIONS:
- Excellent written and verbal communication skills with the ability to explain complex technical concepts and analytical findings to both technical and non\-technical audiences.
- Practical application experience with Python (and associated libraries: pandas, scikit\-learn, etc.), SQL, and statistical analysis.
- Experience with data visualization tools (Matplotlib, Seaborn, Plotly, or similar).
- Advanced proficiency with tree\-based algorithms and dimensionality reduction.
- Experience working with relational databases and developing complex data sets.
- Experience with generative AI concepts, including large language models (LLMs), prompt engineering, retrieval\-augmented generation (RAG), and comfortable with AI coding assistants.
- Experience building, deploying, and managing machine learning solutions using cloud\-native platforms (Azure ML, MLflow, AWS SageMaker, etc.), including MLOps practices such as version control, CI/CD, experiment tracking, model deployment, monitoring, and lifecycle management.
- Commitment to continuous learning, adoption of emerging data science methodologies, and ethical, responsible AI practices.
- Preferred experience in ML model training on multiple GPUs/CUDA.
- Experience optimizing compute performance on large datasets using parallel processing and multi\-core utilization techniques and packages (e.g. Polars, PySpark, Dask, etc.)
- Experience with deep learning libraries (e.g. PyTorch, TensorFlow), transformer\-based architectures, and advanced modeling techniques.
- Strong linear algebra skills.
- Proven experience leading data science projects and teams, mentoring junior data scientists, and driving strategic initiatives.
- Strong analytical skills, research capabilities, and careful attention to detail.
Textron Aviation Inc. must comply with U.S. export control laws and regulations. If a position requires access to sensitive information controlled under these laws and regulations, a successful applicant must be eligible to meet any requirements to access controlled information.
The above statements are intended to describe the general nature and level of work being performed by employees assigned to this job. They are not intended to be an exhaustive list of all responsibilities, duties, and skills required of personnel so classified.
Kansas Tax Credit:
Join Textron Aviation’s Kansas team and you may be eligible for a $5,000 state of Kansas Aviation tax credit for up to five years. Visit https://www.aircapitaloftheworld.com/taxcredits for more information on the tax credit.
EEO Statement
Textron is committed to providing Equal Opportunity in Employment, to all applicants and employees regardless of race, color, religion, age, national origin, military status, veteran status, disability, sex (including pregnancy and sexual orientation), genetic information or any other characteristic protected by law.
Recruiting Company: Textron Aviation
Primary Location: US\-Kansas\-Wichita
Job Function: Business Development
Schedule: Full\-time
Job Level: Individual Contributor
Job Type: Experienced
Shift: First Shift
Job Posting: 08/07/2026, 1:56:30 PM
Job Number: 344102
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 Textron Aviation, 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.
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.
Textron Aviation AI Hiring
Textron Aviation has 1 open AI role right now. They're hiring across Data Scientist. Based in Wichita, KS, US.
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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