Operations Analyst

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  Iterative, prototype-first delivery and MVP prioritization  Direct engagement with shop floor personnel for requirements, data validation, and implementation  Execution across concurrent workstreams in a fast-paced, dynamic environment  Preferred Qualifications Microsoft Fabric or equivalent lakehouse platforms (Databricks, Snowflake): navigation and data extraction  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  Work Environment Office: Desk work, programming, and design tasks.  Shop floor: Machine operation, handling parts, exposure to high noise levels, machine electrical systems, and varying temperature conditions.  Pay Range $100,170 — $137,730 USD What We Offer Our Regular, Full-Time Employees: Holistic Compensation Package: Enjoy a world-class compensation package that includes a competitive salary, equity plan, and discretionary results-based incentive bonus opportunities, ensuring you're truly valued for your contributions. Wellness and Time Off: Embrace a healthy lifestyle with paid vacation, sick time, and company holidays, including a year-end shutdown to recharge. We support gro