Location: Long Beach or Torrance, California, United States. The role includes office work and direct work on the shop floor, including exposure to machine operation, high noise levels, electrical systems, and varying temperatures.
Salary: $100,170–$137,730 USD per year.
Company
Divergent designs, manufactures, and assembles advanced systems for demanding industries through an integrated engineering and manufacturing platform, serving global automotive OEMs and U.S. aerospace and defense companies.
What you will do
•Develop and validate statistical and machine learning models connecting process time, staffing, and engineering data for granular time and cost prediction, initially supporting pricing and later simulation and factory scaling.
•Unify equipment, MES, ERP/BOM, staffing, and engineering data into a shared data model for pricing, simulation, factory scaling, predictive models, and operational tools.
•Define source mappings, metric definitions, data quality rules, and anomaly detection; partner with Data, AI, and Software teams to operationalize data and software tools.
•Identify operational inefficiencies, validate data against shop floor conditions, and implement data-driven improvements with operations and automation teams.
•Lead cross-functional projects from discovery and requirements through prototyping, ROI justification, and implementation.
•Conduct scenario analyses for pricing, capacity, staffing, and investment decisions, and communicate data models, methodology, assumptions, and recommendations to technical and non-technical stakeholders.
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 manufacturing, including work orders, BOMs, routings, and production scheduling.
•Python experience with pandas, NumPy, scikit-learn, statsmodels, matplotlib or Plotly, including data extraction, API integration, and task automation.
•SQL experience with complex joins, window functions, CTEs, relational data modeling, and 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 validation.
•Knowledge of manufacturing metrics including cycle time, yield, throughput, utilization, OEE, and WIP; Git and documented, reproducible code.
•Ability to integrate AI tools into daily workflows, independently own projects, scope requirements, manage timelines and dependencies, align stakeholders, structure ambiguous business problems, communicate technical outputs, prioritize MVPs, engage shop floor personnel, and execute across concurrent workstreams.
Nice to have
•Microsoft Fabric or equivalent lakehouse platforms such as Databricks or Snowflake.
•MES/ERP data structures including work orders, routings, BOMs, genealogy, and NCRs.
•Equipment data flows, IIoT protocols, and time-series data, including OPC-UA, MTConnect, MQTT, or historians.
•Discrete-event simulation with SimPy or AnyLogic and optimization methods such as LP/MIP or OR-Tools.
•Additive manufacturing and metal process chains, including LPBF, heat treatment, CNC machining, and CMM/CT inspection.
•Engineering drawings, GD&T, CAD, cost accounting, pricing, financial modeling, unit economics, margin, or ROI.
•Lean and continuous improvement methods, including Six Sigma, DMAIC, process mapping, and shop floor change management.
•Project management tools such as Atlassian Suite or Smartsheet, Agile methods, technical knowledge transfer, mentorship, and experience in a startup, consulting, or similar high-growth environment.
Culture & Benefits
•Collaborative, dynamic, high-performing, fast-paced, and mission-driven manufacturing environment.
•Paid vacation, sick time, company holidays, and a year-end shutdown.
•Paid parental leave and comprehensive health benefits, including HMO and Premium PPO options.
•Company-sponsored life insurance and short- and long-term disability coverage.
•Learning and development reimbursement, equity plan, and discretionary results-based incentive bonus opportunities.
