Senior Data Scientist – Manufacturing Intelligence, Machine Learning, AI

This is a fully remote position, open to applicants in Washington.

📋 Description

• Design and implement machine learning and statistical models to assist in manufacturing applications such as anomaly detection, quality forecasting, equipment health monitoring, process oversight, throughput enhancement, and decision-making support.

• Utilize supervised, unsupervised, and semi-supervised learning techniques, which include classification, regression, clustering, anomaly detection, time-series analysis, statistical process control, and model interpretability.

• Create anomaly detection systems employing strategies such as control limits, isolation forests, clustering, Mahalanobis distance, autoencoders, time-series models, and supervised classification when labeled defects are accessible.

• Assess model effectiveness using relevant metrics, ground truth definitions, validation methods, and analyses of false positives and false negatives, along with business impact evaluations.

• Recognize situations where data is inadequate, labels are unreliable, ground truth is weak, or a machine learning strategy may not be suitable, and clearly communicate these limitations.

• Collaborate with plant teams and subject matter experts to comprehend process dynamics, verify assumptions, and establish whether model outputs align with actual operational conditions.

⛳️ Requirements

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Industrial Engineering, Mechanical Engineering, Manufacturing Engineering, Operations Research, Applied Mathematics, or a related technical discipline.

• Over 5 years of experience in applying data science, machine learning, statistical modeling, optimization, or advanced analytics within a professional setting.

• Proficient in Python, utilizing libraries such as pandas, NumPy, scikit-learn, SciPy, XGBoost, PyTorch, TensorFlow, statsmodels, or comparable tools.

• Strong SQL capabilities and experience handling large, complex datasets.

• Familiarity with both supervised and unsupervised machine learning techniques, including classification, regression, clustering, anomaly detection, time-series analysis, forecasting, or process optimization.

• Experience in feature engineering from machine, sensor, process, quality, maintenance, production, or operational datasets.

• Familiarity with cloud-based data and analytics platforms such as GCP, AWS, Azure, or similar environments.

• Understanding of MLOps concepts including experiment tracking, model deployment, model monitoring, CI/CD, version control, testing, model registry, and retraining.

• Capability to work with noisy, incomplete, high-frequency, or fragmented operational data.

• Proficient in conveying technical insights clearly to plant teams, engineers, executives, and non-technical stakeholders.

• Ability to function effectively in uncertain environments where requirements, data quality, and success metrics may require clarification.

• Professional confidence to question assumptions, provide constructive feedback, and influence stakeholders based on evidence.

• Proven ability to quickly learn new technical and business domains.

🏝️ Benefits

• Immediate medical, dental, vision, and prescription drug coverage.

• Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized backup childcare, and more.

• Family-building benefits, including adoption and surrogacy expense reimbursement, fertility treatments, and additional support.

• Employee vehicle discount program for employees and family members, as well as management leases.

• Tuition assistance for ongoing education.

• Established and active employee resource groups for community and support.

• Paid time off for both individual and team community service efforts.

• A generous schedule of paid holidays, including the week between Christmas and New Year's Day.

• Paid time off with the option to purchase additional vacation days.