Interested in this Data Scientist role at Cummins?
Apply Now →About This Role
Lead advanced analytics initiatives to solve complex business problems using quantitative analysis, statistical modeling, and machine learning techniques. Design, develop, and deploy machine learning models to support decision systems, recommendation and next best action frameworks, anomaly detection, pattern recognition, and forecasting/time series analysis. Liaise with business stakeholders and leverage knowledge to industrialize and monetize insights from advanced analytics projects. Research, implement, and validate advanced analytical and algorithmic methods, including causal inference, impact measurement, predictive modeling, clustering, and optimization techniques, to quantify business outcomes and evaluate the effectiveness of data driven interventions. Apply statistical inference and data mining techniques to analyze large, diverse datasets and identify meaningful relationships, trends, and drivers of performance. Develop and support scalable, end to end analytics and machine learning solutions, including data processing pipelines and model lifecycle management practices, using commonly used programming languages and distributed data processing frameworks. Support both batch and near real time/streaming data processing in a cloud based analytics environment. Design and maintain modern data platform architectures, including lakehouse style data architectures, with responsibilities for data quality, schema management, and governance controls. Utilize sampling methods and experimental or quasi experimental approaches as needed to support research, analysis, and decision making. Translate analytical results and model insights into clear business language, communicate findings to technical and non technical stakeholders, and partner with domain experts to ensure alignment with business objectives. Lead requirements analysis, assess interdependencies and priorities, and align analytics initiatives with strategic roadmaps and organizational goals. Apply Agile software development practices to design, develop, and enhance analytics solutions. Provide technical leadership and mentorship to less experienced engineers or analysts on data science methodologies, machine learning solution design, and best practices for scalable analytics systems.
Positions require a Master’s degree in Computer or Data Science, Business or Data Analytics, Information Technology or related field and 3 years of experience as a Data Scientist, Data Analyst, IT Consultant or related position. Alternatively, the employer will accept a Bachelor’s degree in Computer or Data Science, Business or Data Analytics, Information Technology or related field and 5 years of experience as a Data Scientist, Data Analyst, IT Consultant or related position. Experience to include: Advanced analytics and quantitative problem solving; Statistical modeling and statistical inference techniques; Machine learning models; Decision systems and recommendation/next-best-action frameworks; Anomaly detection and pattern recognition; Predictive modeling, clustering, and time-series forecasting analysis; Causal inference and business impact measurement methods; Data mining and relationship analysis on large datasets; End-to-end machine learning pipelines and model lifecycle management; Scalable data processing using distributed data processing frameworks; Batch and near real-time/streaming data processing; Modern data platform architectures, including lakehouse-style data architectures; Agile software development practices and technical leadership.
Start Date of Posting : 10/8/26
End Date of Posting: 10/23/26
Location: Columbus, IN 47201 & various unanticipated worksites throughout the U.S .
Position : REMOTE
- Annual USD Salary Minimum – Maximum
$131,000 - $178,000
Job Systems/Information Technology
Organization Cummins Inc.
Role Category Off-site Remote
Job Type Exempt - Experienced
Min Salary $153540
Max Salary $187660
ReqID 2439289
Relocation Package No
100% On-Site No
Cummins and E-Verify
At Cummins, we are an equal opportunity and affirmative action employer dedicated to diversity in the workplace. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, gender, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity and/or expression, or other status protected by law. Cummins validates the right to work using E-Verify and will provide the Social Security Administration (SSA) and, if necessary, the Department of Homeland Security (DHS), with information from each new employee’s Form I-9 to confirm work authorization. Visit http://EEOC.gov to know your rights on workplace discrimination.
Salary Context
This $131K-$187K range is below the median for Data Scientist roles in our dataset (median: $161K across 138 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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Cummins, 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 in Demand for This Role
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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($159K) sits 16% below the category median. Disclosed range: $131K to $187K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Cummins AI Hiring
Cummins has 1 open AI role right now. They're hiring across Data Scientist. Based in Columbus, IN, US. Compensation range: $187K - $187K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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