Divergent is building the new industrial age. Every day, our teams design, manufacture, and assemble advanced systems for some of the world’s most demanding industries. Our integrated engineering and manufacturing platform transforms complex designs into production-ready solutions, helping customers move faster, build smarter, and deliver critical systems when they matter most. Divergent is a qualified Tier 1 supplier to global automotive OEMs and supports leading U.S. aerospace and defense companies.

Join us and help push the boundaries of what can be built.

Purpose

We are seeking a mid-level Operations Analyst with a manufacturing analytics focus to turn production data into operational insight and decision tools. This candidate will apply statistical and machine learning methods to production data and help develop models for granular time and cost prediction, unifying factory data sources into a cohesive data model. The analyst will partner with the central Data team to operationalize this data and, where data does not yet exist, partner with cross-functional teams to drive its exposure. The role spans Manufacturing Intelligence workstreams, including process improvement, simulation, equipment connectivity, and factory scaling, as priorities shift.

The Role

Develop and validate statistical and ML models correlating process time, staffing, and engineering data to enable granular time and cost prediction; initial application in pricing, extending to simulation and factory scaling.
Unify equipment, MES, ERP/BOM, staffing, and engineering data into a cohesive data model that serves as a shared foundation for pricing, simulation, factory scaling, predictive models, and the team’s broader set of tools.
Define source mappings, metric definitions, and data quality rules, including anomaly detection to flag inconsistencies in data inputs (e.g., work orders); partner with the 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 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.

Basic Qualifications

Ability to lawfully access information and technology that is subject to US export controls
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 a manufacturing environment, including work orders, BOMs, routings, and production scheduling
Python: pandas, NumPy, scikit-learn, statsmodels, matplotlib/Plotly; data extraction, API integration, task automation
SQL: 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 for input data validation
Manufacturing operational metrics (cycle time, yield, throughput, utilization, OEE, WIP)
Git; documented, reproducible code
Integration of AI tools into daily workflow
Independent project ownership and management: scoping, requirements gathering, timelines, dependencies, and stakeholder alignment without prescriptive direction
Translation of 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