Data Science R Jobs in Ohio (NOW HIRING)

Job Summary:
Crown Equipment Corporation is a leading innovator in world-class forklift and material handling equipment and technology. They are seeking a Senior Data Scientist to build and maintain scalable data science pipelines, implement MLOps practices, and collaborate with data engineering teams to enhance data science capabilities across the organization.
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.
Qualifications:
Required:
• 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:
• 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.
Company:
Crown is one of the world’s largest material handling companies. Founded in 1945, the company is headquartered in New Bremen, USA, with a team of 10001+ employees. The company is currently Late Stage.