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About This Role
Career Area:
Technology, Digital and DataJob Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
This position will be responsible for building data driven predictive analytics models and applications using automation, artificial intelligence (AI) and machine learning (ML) models to provide actionable recommendations and insights. The position will be part of Caterpillar’s Legal, Security and Public Policy team, tackling challenges and problems that are critical to realizing business value and mitigating high impact enterprise risks. This position will work with data engineers, business analysts, and others as part of a team that assembles large, complex data sets, automates processes and pipelines, builds models and provides insight to key stakeholders.
What You Will Do:
- Demonstrate a breadth of knowledge in the application statistical methods and/or digital methods to solve business problems. Executes data gathering, data mining, and data processing of big data; creating appropriate data models.
- Build and deploy statistical, machine learning, and AI models in a business context.
- Coach and mentor others in the creation, validation, and application of models and digital solutions
- Advocate and implement proper data governance as it applies to machine learning and AI systems.
- Research and apply new methods and models to improve products and exceed customer expectations.
- Focus on continual learning in the Analytics field.
- Clearly explain conclusions to stakeholders with varied backgrounds and experience in quantitative analytical methods. Experience presenting and reporting business insights to management using data visualization technologies.
- Optimize model performance and accuracy to improve effectiveness and business value of data analyses.
What You Will Have:
- Business Statistics: Knowledge of the statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
- Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision.
- Analytical Thinking: Knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
- Machine Learning \& AI: Knowledge of principles, technologies and algorithms of machine learning and AI; ability to develop, implement and deliver related systems, products and services.
+ Completes specific tasks and initiatives utilizing machine learning and artificial intelligence technologies.
+ Applies specific tools in machine learning and AI in Python.
+ Familiarity and demonstrated use of key machine learning, artificial intelligence and agentic frameworks (scikit learn, pytorch, tensorflow, langgraph, etc.)
- Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages.
- Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.
+ Writes, debugs and implements complex queries involving multiple tables or databases.
+ Works with aggregate functions, complex joins, groupings, dynamic and embedded SQL's (Structured Query Languages).
+ Teaches others about query optimization techniques and facilities.
+ Consults on query optimization, interactive queries, testing and verification.
- Requirements Analysis: Knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non\-functionality requirements to ensure the success of a system or software development project.
What Will Set You Apart:
- Masters degree, preferably in data science, statistics, economics, mathematics, or a similar field.
- 4\+ years of experience applying SQL, Python, etc. to solve business challenges
- Experience with visualization in Power BI
- Strong technical and problem\-solving skills and evidence of continuous learning in the analytics field
- Experience leading analytics projects connecting data from various business domains
- Deep Experience with Snowflake \& AWS tools like S3, Lamba, EMR, Glue, Bedrock/Agent Core, etc.
Summary Pay Range:
$112,710\.00 \- $183,140\.00
Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job\-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
- Medical, dental, and vision benefits\*
- Paid time off plan (Vacation, Holidays, Volunteer, etc.)\*
- 401(k) savings plans\*
- Health Savings Account (HSA)\*
- Flexible Spending Accounts (FSAs)\*
- Health Lifestyle Programs\*
- Employee Assistance Program\*
- Voluntary Benefits and Employee Discounts\*
- Career Development\*
- Incentive bonus\*
- Disability benefits
- Life Insurance
- Parental leave
- Adoption benefits
- Tuition Reimbursement
- These benefits also apply to part\-time employees
This position requires working onsite five days a week.
Relocation is available for this position.
Visa Sponsorship is not available for this position.Posting Dates:
July 21, 2026 \- August 3, 2026
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community.
Salary Context
This $112K-$183K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Caterpillar, 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($147K) sits 23% below the category median. Disclosed range: $112K to $183K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Caterpillar AI Hiring
Caterpillar has 2 open AI roles right now. They're hiring across AI Software Engineer, Data Scientist. Positions span Irving, TX, US, Peoria, IL, US. Compensation range: $183K - $258K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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