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Divergent Technologies

End-to-end software-hardware digital manufacturing system

Operations Analyst

Data ScienceConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • Ability to lawfully access information and technology subject to U.S. export controls.
  • Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Operations Research, or a related field, or equivalent experience.
  • Demonstrated experience in operations analytics, analytics engineering, data science, or data-intensive manufacturing roles.
  • Hands-on experience with production data and shop floor execution in a manufacturing environment, including work orders, bills of materials, routings, and production scheduling.
  • Experience using Python, including pandas, NumPy, scikit-learn, statsmodels, matplotlib or Plotly; data extraction, API integration, and task automation.
  • Experience using SQL, including complex joins, window functions, and common table expressions; relational data modeling; cleaning and validation of high-volume operational and time-series data.
  • Applied statistics and supervised machine learning, including hypothesis testing, regression, classification, tree-based ensembles, feature engineering, cross-validation, model evaluation, and anomaly detection for input data validation.
  • Knowledge of manufacturing operational metrics, including cycle time, yield, throughput, utilization, overall equipment effectiveness, and work in process.
  • Experience using Git and producing documented, reproducible code.
  • Experience integrating AI tools into daily workflow.
  • Ability to independently scope and manage projects, including requirements gathering, timelines, dependencies, and stakeholder alignment without prescriptive direction.
  • Ability to translate ambiguous business problems into structured problem statements and analytical approaches.
  • Ability to communicate technical concepts and model outputs as decision-ready recommendations for non-technical stakeholders.
  • Ability to deliver iteratively with a prototype-first approach and prioritize minimum viable products.
  • Ability to engage directly with shop floor personnel for requirements, data validation, and implementation.
  • Ability to execute across concurrent workstreams in a fast-paced, dynamic environment.
  • The work includes desk-based programming and design tasks and shop-floor exposure to machine operation, parts handling, high noise levels, machine electrical systems, and varying temperatures.

Responsibilities

  • Develop and validate statistical and machine learning models correlating process time, staffing, and engineering data to enable granular time and cost prediction, initially for pricing and later for simulation and factory scaling.
  • Unify equipment, manufacturing execution system, enterprise resource planning and bill-of-materials, staffing, and engineering data into a cohesive data model supporting pricing, simulation, factory scaling, predictive models, and the team’s broader tools.
  • Define source mappings, metric definitions, and data quality rules, including anomaly detection to flag inconsistencies in data inputs such as work orders; partner with central Data, AI, and Software teams to operationalize data and software tools.
  • Identify operational inefficiencies and implement data-driven process improvements with operations and automation teams, validating data against shop-floor conditions.
  • Lead cross-functional projects from discovery and requirements through prototyping, business case and return-on-investment justification, and implementation.
  • Conduct ad hoc and scenario analyses supporting pricing, capacity, staffing, and investment decisions.
  • Communicate and document data models, methodology, and assumptions for technical and non-technical stakeholders.

Desired Qualifications

  • Experience with Microsoft Fabric or equivalent lakehouse platforms such as Databricks or Snowflake, including navigation and data extraction.
  • Knowledge of manufacturing execution system and enterprise resource planning data structures, including work orders, routings, bills of materials, genealogy, and non-conformance reports.
  • Knowledge of equipment data flows, industrial internet of things protocols, and time-series data, including OPC-UA, MTConnect, MQTT, and historians.
  • Experience with discrete-event simulation tools such as SimPy or AnyLogic and optimization methods such as linear programming, mixed-integer programming, or OR-Tools.
  • Experience with additive manufacturing and metal process chains, including laser powder bed fusion, heat treatment, CNC machining, and coordinate measuring machine or CT inspection.
  • Knowledge of engineering drawings, geometric dimensioning and tolerancing, and computer-aided design.
  • Knowledge of cost accounting, pricing, and financial modeling, including unit economics, margin, and return on investment.
  • Experience with lean and continuous improvement methods, including Six Sigma, DMAIC, process mapping, and shop-floor change management.
  • Experience with project management tools such as Atlassian Suite or Smartsheet and Agile methods.
  • Experience with technical knowledge transfer and mentorship.
  • Experience in a start-up, consulting, or similar high-growth environment.

About the Company

Divergent Technologies

End-to-end software-hardware digital manufacturing system

Divergent Technologies builds an end-to-end production system for digital manufacturing that combines software and hardware. Its Divergent Adaptive Production System (DAPS) lets clients design parts, 3D print them, and automatically assemble complex structures for sectors like automotive, aerospace, and defense. The system works by letting users optimize designs, reduce material use, and manufacture without large upfront capital investments, using modular production to speed up innovation and lower environmental impact. Divergent differentiates itself by offering a fully integrated, scalable workflow that goes from digital design to finished parts in one system, enabling faster iterations and sustainable manufacturing. The company’s goal is to change how complex structures are built in manufacturing, delivering scalable efficiency and greener production practices.

Company size
501-1,000
Company stage
Series E
Total funding
$1B
Headquarters
Los Angeles, CA
Founded
2014

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