Manufacturing Data Scientist

Company: Allfast Fastening Systems LLC

Primary Location: 15200 Don Julian Road, City of Industry, CA 91745 USA

Workplace Type: Remote

Employment Type: Salaried | Full-Time

Function: Information Systems

Main Duties & Responsibilities

Position Overview
We are seeking a Manufacturing Data Scientist to transformcomplex operational data into actionable insights that improve productivity,quality, cost, reliability, and supply-chain performance. This role willpartner with manufacturing, engineering, quality, supply chain, finance, andinformation technology teams to develop analytical solutions that supportdata-driven decision-making across the organization.
The ideal candidate has strong expertise in Python and SQL,experience working with enterprise resource planning systems, and a practicalunderstanding of manufacturing processes and data. This individual must becomfortable working with large, complex datasets and translating analyticalfindings into clear recommendations for technical and nontechnicalstakeholders.
Key Responsibilities
Analyze manufacturing, production, quality, maintenance, inventory, and supply-chain data to identify trends, risks, inefficiencies, and improvement opportunities.
Build, validate, and maintain data pipelines and reusable analytical datasets using SQL and / or Python
Develop predictive and prescriptive models for applications such as equipment reliability, predictive maintenance, quality forecasting, yield optimization, demand planning, inventory optimization, and production scheduling.
Extract, clean, reconcile, and integrate data from ERP systems, MES, quality systems, equipment sensors, HCM systems, and other operational sources
Partner with manufacturing engineers, plant leaders, quality teams, supply-chain professionals, and business stakeholders to define analytical requirements and measurable success criteria.
Create dashboards, reports, and data visualizations that communicate operational performance and model results clearly.
Conduct root-cause analyses related to production losses, downtime, scrap, rework, throughput, cycle time, and process variation.
Develop and monitor key performance indicators, including overall equipment effectiveness (OEE), first-pass yield, schedule attainment, capacity utilization, downtime, scrap rate, and inventory accuracy.
Deploy analytical models and establish processes for monitoring model performance, data quality, and business impact.
Document data sources, methodologies, assumptions, model limitations, and technical processes.
Promote data literacy and analytical best practices across manufacturing and operations teams.
Ensure analytical solutions comply with applicable data governance, security, quality, and regulatory requirements.

Qualifications

Required Qualifications

SoCal residents strongly preferred with ability to traveloccasionally
Bachelor's degree in data science, statistics, mathematics, computer science, engineering, operations research, or a related quantitative field.
3+ years of professional experience in data science, advanced analytics, machine learning, operations analytics, or a closely related field.
2+ years of experience working with ERP systems and associated operational data, such as production orders, bills of materials (BOMs), routings, inventory, procurement, material movements, costing, or capacity planning.
2+ years of experience working with business intelligence tools like Power BI, Tableau, and Looker
Ability to translate ambiguous / broad objectives into a set of clearly defined problems
Strong written, verbal, and visual communication skills.
Fluency in Python, including experience with common data science and machine-learning libraries such as pandas, NumPy, scikit-learn, or equivalent tools.
Fluency in SQL, including the ability to write complex queries, joins, common table expressions, window functions, aggregations, and data-quality checks.
Demonstrated experience preparing, cleaning, joining, and analyzing large datasets from multiple systems.
Experience applying statistical analysis, machine learning, forecasting, optimization, or anomaly-detection techniques to business or operational problems.
Strong understanding of data validation, model evaluation, experimental design, and statistical reasoning.
Ability to collaborate effectively with both technical teams and manufacturing stakeholders.
Preferred Qualifications
3+ years of experience working in a manufacturing, industrial, automotive, aerospace, medical-device, consumer-products, chemical, semiconductor, or similar production environment.
Knowledge of manufacturing concepts such as Lean manufacturing, Six Sigma, statistical process control, overall equipment effectiveness, process capability, and root-cause analysis.
Working experience with Git, dbt, and LLM APIs
Above and Beyond Qualifications
2+ years of experience working in a fast-paced startup / growth-stage environment
Experience deploying production-grade AI-based workflow automations
3+ years of experience working as Industrial / Manufacturing engineer

Compensation

In compliance with all states and cities requiring transparency of pay, the expected pay range for this position is $100,000 - $135,000.

Compensation can vary depending on several factors, including a candidate's qualifications, skills, experience, competencies, and geographic location. Some roles may qualify for extra incentives like equity, commissions, or other variable performance-related bonuses. Further details will be provided by our recruiting team during the interview process.

Benefits

Benefit offerings include Medical Insurance and Prescription Drugs, Dental Insurance, Vision Insurance, Flexible Spending Accounts, Life Insurance, Short-Term Disability, Long-Term Disability Insurance (for eligible employees), Employee Assistance Plan (EAP), Paid Time Off (may include vacation and sick time), Retirement Program, and Other Voluntary Benefits.