Senior Data Scientist – Manufacturing Intelligence, Machine Learning, AI

🔥 0 minutes ago

☕ Washington – Remote

☕ Washington – Remote

💵 $85.4k - $192k / year

⏰ Full Time

🟠 Senior

📊 Data Scientist

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Ford Motor Company

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1903

🚗 Transport

💰 Post-IPO Debt on 2023-08

Transport • Manufacturing • Sustainability

Ford Motor Company is a globally renowned automotive company based in the United States, established by Henry Ford. The company is committed to building a better world where every individual has the freedom to move and follow their dreams. Ford is dedicated to innovation, with a focus on services, experiences, and software alongside its traditional vehicle manufacturing. The company is actively involved in sustainability initiatives and aims to meet ambitious environmental targets. Ford values service, community impact, and strives to combine business success with social and environmental responsibility. With a rich history of over 121 years, Ford continues to adapt and lead in the evolving automotive landscape.

📋 Description

• Develop machine learning and statistical models to support manufacturing use cases such as anomaly detection, quality prediction, equipment health, process monitoring, throughput improvement, and decision support. • Apply supervised, unsupervised, and semi-supervised learning methods, including classification, regression, clustering, anomaly detection, time-series analysis, statistical process control, and model explainability. • Build anomaly detection solutions using methods such as control limits, isolation forests, clustering, Mahalanobis distance, autoencoders, time-series models, and supervised classification where labeled defects are available. • Evaluate model performance using appropriate metrics, ground truth definitions, validation strategies, false positive and false negative analysis, and business impact measures. • Identify when data is insufficient, labels are unreliable, ground truth is weak, or a machine learning approach is not appropriate, and communicate those limitations clearly. • Partner with plant teams and domain experts to understand process behavior, validate assumptions, and determine whether model outputs reflect real operating 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 field. • 5+ years of experience applying data science, machine learning, statistical modeling, optimization, or advanced analytics in a professional environment. • Strong Python skills using libraries such as pandas, NumPy, scikit-learn, SciPy, XGBoost, PyTorch, TensorFlow, statsmodels, or similar tools. • Strong SQL skills and experience working with large, complex datasets. • Experience with supervised and unsupervised machine learning methods, including classification, regression, clustering, anomaly detection, time-series analysis, forecasting, or process optimization. • Experience building features from machine, sensor, process, quality, maintenance, production, or operational datasets. • Experience working with cloud-based data and analytics platforms such as GCP, AWS, Azure, or similar environments. • Understanding of MLOps concepts such as experiment tracking, model deployment, model monitoring, CI/CD, version control, testing, model registry, and retraining. • Ability to work with noisy, incomplete, high-frequency, or fragmented operational data. • Ability to communicate technical findings clearly to plant teams, engineers, leaders, and non-technical stakeholders. • Ability to operate in ambiguous environments where requirements, data quality, and success criteria may need to be clarified. • Professional confidence to challenge assumptions, push back constructively, and influence stakeholders with evidence. • Demonstrated ability to learn new technical and business domains quickly.

🏖️ Benefits

• Immediate medical, dental, vision and prescription drug coverage • Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more • Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more • Vehicle discount program for employees and family members and management leases • Tuition assistance • Established and active employee resource groups • Paid time off for individual and team community service • A generous schedule of paid holidays, including the week between Christmas and New Year's Day • Paid time off and the option to purchase additional vacation time