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Salary: USD 100,170 - 137,730 / annual
Divergent is building an integrated engineering and manufacturing platform that transforms complex designs into production-ready solutions for automotive OEMs and aerospace/defense companies. We are seeking a mid-level Operations Analyst with a manufacturing analytics focus to turn production data into operational insight and decision tools.
In this role, you will apply statistical and machine learning methods to production data to develop models for granular time and cost prediction. You will unify factory data sources (equipment, MES, ERP/BOM, staffing, engineering) into a cohesive data model serving as a shared foundation for pricing, simulation, factory scaling, and predictive tools.
Key responsibilities include:
- Develop and validate statistical and ML models correlating process time, staffing, and engineering data to enable time and cost prediction, with initial application in pricing and extension to simulation and factory scaling
- Unify equipment, MES, ERP/BOM, staffing, and engineering data into a cohesive data model
- Define source mappings, metric definitions, and data quality rules, including anomaly detection; partner with central Data, AI, and Software teams to operationalize data and 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 ROI 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
You will work in both office (desk work, programming, design) and shop floor environments (machine operation, handling parts, exposure to noise, electrical systems, varying temperatures).
REQUIREMENTS:
- Bachelor's or Master's in Data Science, Statistics, CS, Operations Research, or 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 proficiency: pandas, NumPy, scikit-learn, statsmodels, matplotlib/Plotly; data extraction, API integration, task automation
- SQL expertise: complex joins, window functions, CTEs; relational data modeling; cleaning and validation of high-volume operational and time-series data
- Applied statistics and supervised ML: hypothesis testing, regression, classification, tree-based ensembles; feature engineering, cross-validation, model evaluation; anomaly detection
- Manufacturing operational metrics knowledge (cycle time, yield, throughput, utilization, OEE, WIP)
- Git and documented, reproducible code
- Integration of AI tools into daily workflow
- Independent project ownership and management: scoping, requirements gathering, timelines, dependencies, stakeholder alignment
- Ability to translate ambiguous business problems into structured problem statements and analytical approaches
- Communication of technical concepts and model outputs as decision-ready recommendations for non-technical stakeholders
- Iterative, prototype-first delivery and MVP prioritization
- Direct engagement with shop floor personnel for requirements, data validation, and implementation
- Execution across concurrent workstreams in fast-paced, dynamic environment
- Ability to lawfully access information and technology subject to US export controls
PREFERRED QUALIFICATIONS:
- Microsoft Fabric or equivalent lakehouse platforms (Databricks, Snowflake)
- MES/ERP data structures (work orders, routings, BOMs, genealogy, NCRs)
- Equipment data flows, IIoT protocols, and time-series data (OPC-UA, MTConnect, MQTT, historians)
- Discrete-event simulation (SimPy, AnyLogic) and optimization methods (LP/MIP, OR-Tools)
- Additive manufacturing and metal process chains (LPBF, heat treatment, CNC machining, CMM/CT inspection)
- Engineering drawings, GD&T, and CAD
- Cost accounting, pricing, and financial modeling (unit economics, margin, ROI)
- Lean and continuous improvement methods (Six Sigma, DMAIC, process mapping) and shop floor change management
- Project management tooling (Atlassian Suite, Smartsheet) and Agile methods
- Technical knowledge transfer and mentorship
- Start-up, consulting, or similar high-growth environment experience
