Job Description

Role Description

We are looking for a Senior Data Scientist to help build advanced analytics, machine learning, and AI solutions for manufacturing operations. This role will focus on using factory data to detect anomalies, improve quality, reduce downtime, optimize throughput, and support reusable data models that connect fragmented manufacturing systems into a common intelligence layer.

The ideal candidate has strong applied machine learning skills, practical experience working with complex operational data, and the ability to partner with manufacturing, data engineering, platform, and software teams to move analytical solutions toward production. This is not a pure research role. We are looking for someone who can move from problem framing to data understanding, model development, validation, stakeholder alignment, and production support.

The candidate should be able to learn unfamiliar domains quickly, challenge assumptions constructively, and push back when requirements, data quality, or model expectations are not realistic. Manufacturing experience is strongly preferred, but we are also open to candidates from adjacent industrial, operations, quality, aerospace, semiconductor, supply chain, or equipment-heavy environments who can learn the manufacturing domain quickly.

Qualifications

• 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.
• Professional confidence to challenge assumptions, push back constructively, and influence stakeholders with evidence.
• Demonstrated ability to learn new technical and business domains quickly.

Requirements

• Experience applying data science or machine learning in manufacturing, industrial, automotive, aerospace, semiconductor, supply chain, quality, maintenance, or operations environments.
• Experience with automotive manufacturing, stamping, body shop, paint shop, final assembly, battery manufacturing, or powertrain operations.
• Understanding of manufacturing KPIs such as throughput, cycle time, downtime, OEE, JPH, FTT, FRC, scrap, rework, takt time, bottlenecks, quality escapes, and safety events.
• Basic understanding of manufacturing systems such as MES, SCADA, PLCs, historians, CMMS, QMS, ERP, or industrial IoT platforms.
• Familiarity with graph databases or semantic technologies such as RDF, OWL, SPARQL, Neo4j, Stardog, GraphDB, or similar tools.

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.

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