Principal Data Scientist

PrincipalData 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 aPrincipalData Scientist / ML Engineerto develop, deploy, and scale advanced analytics and machine learning solutions across manufacturing and supply chain use cases. This role is ideal for someone who combinesa strong foundationin statistics, mathematics, and machine learning with hands-on software engineering andMLOpsskills.

The ideal candidate can frame ambiguous business problems, work closely with domain experts, build robust models, andproductionizesolutions 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 applyappropriate modelingapproaches based onthe 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.

Productionizationand ML Engineering

• Design, build, deploy, andmaintainproduction-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 solutionsremainaccurateand reliable in production.

Business and Cross-Functional Partnership

• Partner with manufacturing engineers, supply chain teams, planners, IT, and analytics stakeholders toidentifyhigh-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 driveadoptionof data science and ML by building trust,demonstratingvalue, 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, ora 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 andproductionML 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 withMLOpsconcepts 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 communicationskills 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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