Principal Data Scientist

Principal Data Scientist / ML Engineer

Analog Devices is a global leader in semiconductor innovation, bridging the physical and digital worlds to enable breakthroughs at the Intelligent Edge. Our technologies help customers interpret the world around us and build solutions that improve efficiency, sustainability, and performance across industries. 

Within our manufacturing and supply chain organizations, data, analytics, and machine learning play a critical role in improving operational performance, increasing resilience, accelerating decision-making, and unlocking new value from our data. We are looking for a full stack data scientist / ML engineer who can move seamlessly from exploratory analysis and model development to production deployment and operationalization. 

Role Overview 

We are seeking a Principal Data Scientist / ML Engineer to develop, deploy, and scale advanced analytics and machine learning solutions across manufacturing and supply chain use cases. This role is ideal for someone who combines a strong foundation in statistics, mathematics, and machine learning with hands-on software engineering and MLOps skills. 

The ideal candidate can frame ambiguous business problems, work closely with domain experts, build robust models, and productionize solutions that are reliable, maintainable, and impactful. This person should be comfortable writing high-quality code, designing data and ML pipelines, deploying models into production environments, and monitoring solutions over time to ensure sustained business value. 

Key Responsibilities 

Machine Learning and Advanced Analytics 

• Develop statistical, machine learning, and optimization solutions for manufacturing and supply chain problems such as forecasting, anomaly detection, classification, root cause analysis, inventory optimization, scheduling support, and quality/yield improvement. 

• Select and apply appropriate modeling approaches based on the business problem, data availability, operational constraints, and explainability requirements. 

• Perform exploratory data analysis, feature engineering, model evaluation, and error analysis to generate actionable insights and improve model performance. 

• Translate complex analytical findings into practical recommendations for technical and business stakeholders. 

Productionization and ML Engineering 

• Design, build, deploy, and maintain production-grade ML solutions, APIs, batch scoring pipelines, and decision-support applications. 

• Implement scalable and maintainable ML pipelines for training, validation, inference, monitoring, and retraining. 

• Apply software engineering best practices including modular design, version control, testing, code reviews, CI/CD, and documentation. 

• Partner with data engineers and platform teams to integrate models into enterprise data and application ecosystems. 

• Establish model monitoring, drift detection, performance tracking, and alerting to ensure solutions remain accurate and reliable in production. 

Business and Cross-Functional Partnership 

• Partner with manufacturing engineers, supply chain teams, planners, IT, and analytics stakeholders to identify high-value problems and convert them into deployable solutions. 

• Balance experimentation with practical execution, focusing on use cases that can deliver measurable business impact. 

• Communicate clearly with both technical and non-technical audiences, including explaining assumptions, tradeoffs, risks, and expected outcomes. 

• Help drive adoption of data science and ML by building trust, demonstrating value, and creating solutions that fit real operational workflows. 

Qualifications 

Required Qualifications 

• Bachelor’s, Master’s, or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Operations Research, Industrial Engineering, Electrical Engineering, or a related quantitative field. 

• Strong background in machine learning, statistics, probability, and applied mathematics. 

• Experience developing and deploying machine learning models in production environments. 

• Strong programming skills in Python and/or similar languages used in data science and production ML systems. 

• Experience with software engineering best practices such as Git, testing, code reviews, packaging, and CI/CD. 

• Experience building data pipelines and working with large-scale datasets in cloud or enterprise environments. 

• Familiarity with MLOps concepts such as model versioning, experiment tracking, deployment workflows, model monitoring, and retraining. 

• Ability to work across the full ML lifecycle, from problem framing and experimentation through deployment and operational support. 

• Strong communication skills and the ability to collaborate effectively with domain experts and cross-functional teams. 

Preferred Qualifications 

• Experience in semiconductor manufacturing, industrial analytics, operations, or supply chain domains. 

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