Data Scientist – Manufacturing & Supply Chain Intelligence

DynPro Inc.; Fremont (CA); Hybrid; USD 120,000 - 180,000; Full time

Job summary

DynPro Inc. is seeking an experienced Data Scientist to build and enhance machine learning solutions for manufacturing and supply chain intelligence. Hybrid role based in Fremont, CA, focusing on a cognitive intelligence layer with normal behavior models, anomaly detection, and human-in-the-loop learning.

The ideal candidate has 4+ years of enterprise ML experience, hands-on model development, and strong cross-functional collaboration skills to translate business needs into analytical solutions.

Qualifications

• Master's or PhD in Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Engineering, or related field.
• 4+ years applying machine learning, deep learning, NLP, or advanced analytics in an enterprise environment.
• Hands-on experience developing and tuning machine learning models.
• Experience with anomaly detection or Normal Behavior Models.
• Experience developing predictive models or should-cost/cost estimation models.
• Experience with entity resolution, entity matching, record linkage, or data matching techniques.
• Experience incorporating human feedback, overrides, or corrections into model retraining cycles.
• Strong understanding of model evaluation, confidence scoring, feature engineering, and optimization.
• Strong analytical and problem-solving skills with the ability to work with large datasets.
• Experience working with cross-functional technical and business teams.

Responsibilities

• Design, build, and tune machine learning and predictive models for enterprise use cases.
• Develop Normal Behavior Models to identify unusual patterns, anomalies, and deviations in data.
• Build and improve anomaly detection models for manufacturing, supply chain, procurement, or operational datasets.
• Develop entity resolution and matching models to identify, consolidate, and connect related entities across sources.
• Build should-cost and predictive cost models to support pricing, procurement, sourcing, or cost intelligence use cases.
• Analyze large and complex datasets to identify trends, patterns, relationships, and business insights.
• Incorporate human-in-the-loop feedback into machine learning models.
• Retrain and refine models based on user overrides, corrected confidence scores, and business feedback.
• Continuously evaluate model performance, accuracy, confidence levels, and effectiveness.
• Partner with engineering, product, supply chain, procurement, manufacturing, and business teams to translate requirements.
• Support deployment, monitoring, validation, and continuous improvement of machine learning models in production environments.
• Document model logic, assumptions, methodology, results, and performance metrics.

Skills

Data science

Machine learning

Anomaly detection

Entity resolution

Cost modeling

NLP

Human-in-the-loop

Education

Master's degree in Data Science / Statistics / CS / ML / Math / Engineering

PhD in a quantitative field

Tools

Python

SQL

TensorFlow/PyTorch

Job description

Data Scientist – Manufacturing & Supply Chain Intelligence

Location: Fremont, CA – Hybrid

Role Overview

We are looking for an experienced Data Scientist to build and enhance advanced machine learning solutions supporting manufacturing and supply chain intelligence.

This role will focus on developing and tuning a Cognitive Intelligence layer, including Normal Behavior Models, anomaly detection, entity resolution, should-cost models, predictive analytics, and human-in-the-loop learning.

The ideal candidate will have strong hands-on experience building production-grade machine learning models and improving model accuracy based on business-user feedback and overrides.

Key Responsibilities

• Design, build, and tune machine learning and predictive models for enterprise use cases.
• Develop Normal Behavior Models to identify unusual patterns, anomalies, and deviations in business or operational data.
• Build and improve anomaly detection models for manufacturing, supply chain, procurement, or operational datasets.
• Develop entity resolution and matching models to identify, consolidate, and connect related entities across multiple data sources.
• Build should-cost and predictive cost models to support pricing, procurement, sourcing, or cost intelligence use cases.
• Analyze large and complex datasets to identify trends, patterns, relationships, and business insights.
• Incorporate human-in-the-loop feedback into machine learning models.
• Retrain and refine models based on user overrides, corrected confidence scores, and business feedback.
• Continuously evaluate model performance, accuracy, confidence levels, and effectiveness.
• Partner with engineering, product, supply chain, procurement, manufacturing, and business teams to understand requirements and translate them into analytical solutions.
• Support deployment, monitoring, validation, and continuous improvement of machine learning models in production environments.
• Document model logic, assumptions, methodology, results, and performance metrics.

Required Qualifications

• Master's or PhD in Data Science, Statistics, Computer Science, Machine Learning, Mathematics, Engineering, or another related quantitative field.
• 4+ years of experience applying machine learning, deep learning, NLP, or advanced analytics in an enterprise environment.
• Strong hands-on experience developing and tuning machine learning models.
• Experience with anomaly detection or Normal Behavior Models.
• Experience developing predictive models or should-cost/cost estimation models.
• Experience with entity resolution, entity matching, record linkage, or data matching techniques.
• Experience incorporating human feedback, overrides, or corrections into model retraining and improvement cycles.
• Strong understanding of model evaluation, confidence scoring, feature engineering, and model optimization.
• Strong analytical and problem-solving skills with the ability to work with complex and large datasets.
• Experience working with cross-functional technical and business teams.

Preferred Qualifications

• Experience within manufacturing, semiconductor, supply chain, procurement, sourcing, or industrial analytics.
• Experience working with supplier, material, procurement, manufacturing, or cost-related datasets.
• Exposure to production machine learning environments and model monitoring.
• Experience with advanced statistical modeling, NLP, deep learning, or AI-based decision-support systems.