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About This Role
Company Description:
Crown Equipment Corporation is a leading innovator in world\-class forklift and material handling equipment and technology. As one of the world’s largest lift truck manufacturers, we are committed to providing the customer with the safest, most efficient and ergonomic lift truck possible to lower their total cost of ownership.
Job Posting External
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Primary Responsibilities
- Build and maintain scalable data science pipelines and workflows using modern tools and frameworks.
- Implement MLOps practices to ensure model reproducibility, monitoring, and lifecycle management.
- Collaborate with data engineering teams to ensure data quality, accessibility, and pipeline reliability.
- Deploy models into production environments and monitor performance over time.
- Develop and maintain code libraries, documentation, and best practices for data science work.
- Identify high\-value opportunities where advanced analytics can drive business outcomes.
- Evaluate and recommend new tools, technologies, and approaches to enhance data science capabilities.
- Lead proof\-of\-concept initiatives to demonstrate the value of innovative analytical approaches.
- Establish standards and frameworks for data science work across the organization
- Design, develop, and deploy predictive and prescriptive models to address key business challenges in manufacturing, supply chain, quality, and operations.
- Build machine learning models for demand forecasting, production optimization, predictive maintenance, quality prediction, and yield improvement.
- Develop statistical models to identify root causes of process variations, defects, and operational inefficiencies.
- Create optimization algorithms for resource allocation, production scheduling, and inventory management.
- Apply natural language processing and computer vision techniques where applicable to manufacturing use cases.
- Conduct A/B testing and experimental design to validate hypotheses and measure impact of interventions.
- Partner with business stakeholders across manufacturing, operations, supply chain, quality, and maintenance to understand requirements and pain points.
- Work closely with data engineers, analysts, and IT teams to integrate data science solutions into business processes.
Minimum Qualifications
- Bachelor’s degree in Computer Science, Data Science, Statistics, or a related field, along with at least 5 years of experience
- + *Non\-degree considered if 12\+ years of related experience along with a high school diploma or GED*
Preferred Qualifications
- 6 years of relevant project experience in successfully launching, planning, and executing data science projects, including statistical analysis, data engineering, and data visualization.
- Experience leading projects that apply ML and data science to business functions.
- Fluency in multiple programming languages and statistical analysis tools such as Python, C\+\+, JavaScript, R, SAS, Excel, SQL, MATLAB, SPSS.
- Knowledge of statistical and data mining techniques such as GLM/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, CNN, RNN.
- Strong understanding of AI, its potential roles in solving business problems, and the future trajectory of generative AI models.
- MS in quantitative discipline (Computer Science, Data Science, Statistics or related fields).
- Knowledge of Six Sigma, Lean Manufacturing, or related process improvement methodologies.
- Experience with predictive maintenance, quality analytics, or process optimization in manufacturing.
- Familiarity with IIoT platforms and edge computing.
- Experience with computer vision applications for quality inspection or process monitoring.
- Publications or presentations at data science conferences or in peer\-reviewed journals.
- Certifications in cloud platforms (Azure, AWS) or data science specializations.
Work Authorization:
Crown will only employ those who are legally authorized to work in the United States. This is not a position for which sponsorship will be provided. Individuals with temporary visas or who need sponsorship for work authorization now or in the future, are not eligible for hire.
No agency calls please.
Compensation and Benefits:
Crown offers an excellent wage and benefits package for full\-time employees including Health/Dental/Vision/Prescription Drug Plan, Flexible Benefits Plan, 401K Retirement Savings Plan, Life and Disability Benefits, Paid Parental Leave, Paid Holidays, Paid Vacation, Tuition Reimbursement, and much more.
EOE Veterans/Disabilities
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Crown Equipment, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Crown Equipment AI Hiring
Crown Equipment has 1 open AI role right now. They're hiring across Data Scientist. Based in New Bremen, OH, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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