Senior Data Scientist

Crown Equipment Corporation•New Bremen, OH

13d

About The Position

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. This role involves building and maintaining scalable data science pipelines and workflows, implementing MLOps practices, collaborating with data engineering teams, deploying models into production, and developing code libraries and documentation. The Senior Data Scientist will identify high-value opportunities for advanced analytics, evaluate new tools and technologies, lead proof-of-concept initiatives, and establish standards for data science work. Key responsibilities include designing, developing, and deploying predictive and prescriptive models for manufacturing, supply chain, quality, and operations, including machine learning models for forecasting, optimization, and prediction. The role also involves developing statistical models for root cause analysis, creating optimization algorithms, and applying NLP and computer vision techniques. Conducting A/B testing and experimental design, partnering with business stakeholders, and working closely with data engineers, analysts, and IT teams are also crucial aspects of this position.

Requirements

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

Nice To Haves

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.

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.