Operations Analyst

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. 

• 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. 

• 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 

• 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 

• 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.