Senior Data Scientist

Responsibilities

• check_circle Build and maintain scalable data science pipelines and workflows using modern tools and frameworks.
• check_circle Implement MLOps practices to ensure model reproducibility, monitoring, and lifecycle management.
• check_circle Collaborate with data engineering teams to ensure data quality, accessibility, and pipeline reliability.
• check_circle Deploy models into production environments and monitor performance over time.
• check_circle Develop and maintain code libraries, documentation, and best practices for data science work.
• check_circle Identify high-value opportunities where advanced analytics can drive business outcomes.
• check_circle Evaluate and recommend new tools, technologies, and approaches to enhance data science capabilities.
• check_circle Lead proof-of-concept initiatives to demonstrate the value of innovative analytical approaches.
• check_circle Establish standards and frameworks for data science work across the organization
• check_circle Design, develop, and deploy predictive and prescriptive models to address key business challenges in manufacturing, supply chain, quality, and operations.
• check_circle Build machine learning models for demand forecasting, production optimization, predictive maintenance, quality prediction, and yield improvement.
• check_circle Develop statistical models to identify root causes of process variations, defects, and operational inefficiencies.
• check_circle Create optimization algorithms for resource allocation, production scheduling, and inventory management.
• check_circle Apply natural language processing and computer vision techniques where applicable to manufacturing use cases.
• check_circle Conduct A/B testing and experimental design to validate hypotheses and measure impact of interventions.
• check_circle Partner with business stakeholders across manufacturing, operations, supply chain, quality, and maintenance to understand requirements and pain points.
• check_circle Work closely with data engineers, analysts, and IT teams to integrate data science solutions into business processes.

Basic 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.

Benefits