Operations Analyst

Divergent • Long Beach, CA

17d • $100,170 - $137,730

About The Position

Divergent is seeking a mid-level Operations Analyst focused on manufacturing analytics. The role turns production data into operational insights and decision tools by applying statistical and machine learning methods to production data, developing granular time and cost prediction models, and unifying factory data sources into a cohesive data model. The analyst partners with the central Data team to operationalize data and collaborates with cross-functional teams to expose data that is not yet available. Work spans Manufacturing Intelligence priorities such as process improvement, simulation, equipment connectivity, and factory scaling.

The Operations Analyst develops and validates models linking process time, staffing, and engineering data; builds shared data foundations from equipment, MES, ERP/BOM, staffing, and engineering sources; defines data mappings, metrics, and quality rules; and collaborates with Data, AI, Software, operations, and automation teams. The role also leads cross-functional projects from discovery through implementation, conducts ad hoc and scenario analyses, and documents methods and assumptions for technical and non-technical stakeholders.

The work environment includes office desk work, programming, and design tasks, as well as shop floor exposure to machine operation, parts handling, high noise, machine electrical systems, and varying temperatures. The listed pay range is $100,170–$137,730 USD.

Requirements

• Ability to lawfully access information and technology subject to US 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 a manufacturing environment, including work orders, BOMs, routings, and production scheduling.
• Python experience with pandas, NumPy, scikit-learn, statsmodels, matplotlib or Plotly, data extraction, API integration, and task automation.
• SQL skills including 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 skills, including hypothesis testing, regression, classification, tree-based ensembles, feature engineering, cross-validation, model evaluation, and anomaly detection for input data validation.
• Knowledge of manufacturing operational metrics such as cycle time, yield, throughput, utilization, OEE, and WIP.
• Experience with Git and documented, reproducible code.
• Ability to integrate AI tools into daily workflow.
• Ability to independently scope and manage projects, gather requirements, manage timelines and dependencies, and align stakeholders without prescriptive direction.
• Ability to translate ambiguous business problems into structured problem statements and analytical approaches.
• Ability to communicate technical concepts and model outputs as decision-ready recommendations for non-technical stakeholders.
• Experience with iterative, prototype-first delivery and MVP prioritization.
• Ability to engage directly with shop floor personnel for requirements, data validation, and implementation.
• Ability to execute across concurrent workstreams in a fast-paced, dynamic environment.

Nice To Haves

• Microsoft Fabric or equivalent lakehouse platforms such as Databricks or Snowflake, including navigation and data extraction.
• Familiarity with MES/ERP data structures, including work orders, routings, BOMs, genealogy, and NCRs.
• Knowledge of equipment data flows, IIoT protocols, and time-series data, including OPC-UA, MTConnect, MQTT, and historians.
• Experience with discrete-event simulation tools such as SimPy or AnyLogic and optimization methods such as LP/MIP or OR-Tools.
• Knowledge of additive manufacturing and metal process chains, including LPBF, heat treatment, CNC machining, and CMM/CT inspection.
• Familiarity with engineering drawings, GD&T, and CAD.
• Experience with cost accounting, pricing, and financial modeling, including unit economics, margin, and ROI.
• Knowledge of Lean and continuous improvement methods, including Six Sigma, DMAIC, process mapping, and shop floor change management.
• Familiarity with project management tooling such as Atlassian Suite or Smartsheet and Agile methods.
• Technical knowledge transfer and mentorship experience.
• Experience in a start-up, consulting, or similar high-growth environment.

Responsibilities

• Develop and validate statistical and machine learning models correlating process time, staffing, and engineering data to support granular time and cost prediction, initially for pricing and later for simulation and factory scaling.
• Unify equipment, MES, ERP/BOM, staffing, and engineering data into a cohesive shared data model for pricing, simulation, factory scaling, predictive models, and related tools.
• Define source mappings, metric definitions, and data quality rules, including anomaly detection to flag inconsistent inputs such as work orders.
• Partner with 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.

Benefits

• Competitive compensation package with an equity plan and discretionary results-based incentive bonus opportunities.
• Paid vacation, sick time, and company holidays, including a year-end shutdown.
• Paid parental leave.
• Comprehensive health and wellness benefits, including HMO and Premium PPO options.
• Company-sponsored life insurance.
• Short- and long-term disability coverage.
• Reimbursement opportunities for learning and development initiatives.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level