Analytics Lead, Manufacturing Quality

6 дн. назад

USALeadOnsite

aicomputer visionsensor fusiondata streamsreal-time analytics

Lead analytics efforts in manufacturing quality at , a defense technology company focused on autonomous systems and AI.

О компании

Quality Intelligence supports data and AI in 's manufacturing quality organization through three distinct areas: Analytics, Manufacturing AI, and Vision Inspection. Analytics owns end-to-end data and analytics work that directly drives impact for 's product quality engineers on the factory floor. Our customers are program quality leaders, manufacturing engineers, and operators across sites and programs. The work encompasses building scorecards that catch quality drift before customers do, pipelines that turn ERP / MES / QMS data into decisions, and AI tools that compress hours of manual triage into minutes.
We operate hub-and-spoke. HQ builds platform-grade analytics centrally; site engineers localize and run them at each manufacturing site. This is the site applications lead role at Arsenal-1 , 's high-volume manufacturing site in Ashville, OH. You are the resident analytics engineer at the site and the person accountable for what Quality Intelligence has deployed there. Most of this role is hands-on build; the rest is running the site as a program: gathering requirements from the floor, sequencing the work, driving rollouts to completion, and feeding what you learn back into the HQ roadmap. Arsenal 1 is a lighthouse site, so the patterns you set here become the template for the factories that follow.
This role is subject to ITAR. Applicants must be eligible to obtain and maintain a U.S. Government security clearance.

Требования

Bachelor's degree in Computer Science, Mechanical Engineering, Industrial Engineering, or a related technical field from an accredited engineering program.
4+ years in a Data Engineer, Analytics Engineer, or similar role with at least 2 years applied data engineering experience in a manufacturing or hardware product engineering environment.
You understand how manufacturing works: the workflows, the quality gates, and how manufacturing data drives or degrades quality outcomes. You are willing to work in and around manufacturing operations, including time on the production floor.
Production experience with Foundry, Databricks or an equivalent cloud lake house. You have built and maintained pipelines and dashboards other teams depend on. Strong SQL on large, multi-source datasets: joins across heterogeneous systems, window functions, and performance tuning.
Strong applied experience using AI (Cursor, Claude Code, Copilot, AIP) with ability to review AI-generated artifacts critically. You are aware of the mistakes AI tools can make, with a clear view of where they help and where they don't.
Strong Python for data transformation and scripting (Pandas, PySpark, or equivalent).
Demonstrated root-cause analysis on complex data issues. When a number looks wrong, you can trace it back through the stack and explain why.
Eligible to obtain and maintain a U.S. Government security clearance (this role is subject to ITAR).
You communicate plainly: to a director without jargon, to an engineer without losing precision.
Based in or willing to relocate to the greater Columbus, OH area and work on-site at Arsenal 1 in Ashville, OH.
Travel up to 25% to sites and vendors.

Будет плюсом

Experience supporting analytics for hardware manufacturing (NPI, ramp, high-volume) across any of ERP (Oracle, NetSuite, SAP), MES, QMS, PLM (Teamcenter), or inventory / warehouse systems.
Experience as an embedded or site-resident engineer at a manufacturing or industrial site, or standing up systems and data at a greenfield facility.
Familiarity with quality methodologies: RCCA / 8D, FMEA, GD&T, IQC / OQC, control-plan design.
Defense or regulated-manufacturing experience (ITAR, AS9100, IPC-610, MIL-STD-1916, or similar).
Software engineering practices: Git, code review, CI, and testing data code with the same rigor as application code.
Experience integrating LLMs or ML models into analytics workflows: RAG over operational data, AI-assisted triage, or agentic data exploration.
Experience mentoring or leading a small team of engineers or analysts.
The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of 's total compensation package. Additionally, offers top-tier benefits for full-time employees, including:

Условия

At , we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits .

Другое

Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, is changing how military systems are designed, built and sold. ’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.
ABOUT THE SITE
, a leader in autonomous systems and weapons manufacturing, is building  Arsenal-1 , its first hyperscale manufacturing facility, just south of Columbus, Ohio in Pickaway County. Arsenal-1 is redefining the scale and speed that autonomous systems and weapons can be produced for the United States and its allies and partners. This is a monumental and essential step toward rebuilding America’s defense industrial base, strengthening America’s warfighting capabilities, and enhancing deterrence amid rising international threats.
Intersection of Analytics and Manufacturing: You will operate at the intersection of hardware manufacturing and data analytics. You are not afraid to spend time on the shop floor analyzing quality workflows, building analytics tools for said workflows and implementing them in well-designed, actionable dashboards.
Production Data: You'll pull from production systems (ERP, MES, QMS, inventory), build pipelines and ontologies in Palantir Foundry and Databricks, and partner with manufacturing engineers, ML practitioners, and program quality leads to ship analytics products operators depend on.
Develop & Operate Analytics for Arsenal 1: You will design, build, and operate the production dashboards, pipelines, and quality metrics inspection-data analytics the site runs on. Arsenal 1 is greenfield, so you will collaborate closely with HQ to port over proven work. Well thought out decisions you make set the pattern for future programs.
Run intake and priorities for the site: hold a standing feedback loop with operators, manufacturing engineers, and program quality. Turn what you hear into a prioritized, visible backlog — what you can configure this week, what needs HQ build time, and what we are deliberately not doing — and file crisp, evidence-backed asks back to HQ so the platform gets better.
Investigate data quality: When a dashboard is inaccurate or a number looks wrong, you are the lead investigator. Deep-dive analysis in SQL and Python, trace problems through the stack, identify the root cause, and fix it at the source.
Drive technical improvements: You will implement robust data-quality checks, validation rules, and automated monitoring directly in the pipelines. Your data is trusted because you made it provably trustworthy.
Build AI-assisted analytics tools: small apps and workflows in Foundry / Databricks that reduce repetitive analyst work by 10x, grounded in what you have learned from operators on the floor.
Lead data projects end-to-end: partner with cross-functional teams from requirements through deployment. Translate program quality leads' problems into data products that already exist or can be configured quickly and own the rollout.
Drive adoption: a dashboard nobody opens is ineffective. You will train operators, run office hours, track usage, and treat adoption at Arsenal 1 as a deliverable you own, not a downstream side effect.
AI Use: You will be expected to use AI aggressively in your own work: to draft pipelines, write tests, generate dashboards, explore unfamiliar data, and accelerate the repetitive parts of the job.
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