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 and developing models for granular time and cost prediction. It also unifies factory data sources into a cohesive data model. The analyst partners with the central Data team to operationalize data and works with cross-functional teams to expose data that is not yet available. Work spans Manufacturing Intelligence initiatives, including process improvement, simulation, equipment connectivity, and factory scaling, as priorities shift.

The analyst develops and validates models that relate process time, staffing, and engineering data, initially supporting pricing and later simulation and factory scaling. The role defines mappings, metrics, and data quality rules; collaborates with Data, AI, Software, operations, and automation teams; and validates analysis against shop-floor conditions. It includes leading projects from discovery and requirements through prototyping, ROI justification, and implementation, as well as providing scenario analyses and communicating methods and findings to technical and non-technical stakeholders.

The work environment includes office-based desk work, programming, and design tasks, as well as shop-floor work involving machine operation, handling parts, high noise levels, machine electrical systems, and varying temperatures. The posted pay range is $100,170–$137,730 USD. Divergent describes a compensation package with salary, equity, and discretionary results-based incentive bonus opportunities; paid vacation, sick time, company holidays and year-end shutdown; paid parental leave; health and wellness benefits including HMO and Premium PPO options; company-sponsored life insurance and short- and long-term disability coverage; and reimbursement opportunities for learning and development.

Requirements

• Ability to lawfully access information and technology subject to U.S. 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, including pandas, NumPy, scikit-learn, statsmodels, and matplotlib or Plotly; data extraction, API integration, and task automation.
• SQL experience with complex joins, window functions, and CTEs; relational data modeling; and cleaning and validation of high-volume operational and time-series data.
• Applied statistics and supervised machine learning experience, 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, including 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 delivering iteratively using prototypes and prioritizing MVPs.
• Ability to execute across concurrent workstreams in a fast-paced, dynamic environment.

Nice To Haves

• Experience navigating and extracting data from Microsoft Fabric or equivalent lakehouse platforms such as Databricks or Snowflake.
• Knowledge of 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.
• Experience with additive manufacturing and metal process chains, including LPBF, heat treatment, CNC machining, and CMM/CT inspection.
• Knowledge of 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.
• Experience with project management tools such as Atlassian Suite or Smartsheet and Agile methods.
• Experience in technical knowledge transfer and mentorship.
• 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 for 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 shared, cohesive data model.
• 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 gathering through prototyping, business-case and ROI justification, and implementation.
• Conduct ad hoc and scenario analyses to support pricing, capacity, staffing, and investment decisions.
• Communicate and document data models, methodology, and assumptions for technical and non-technical stakeholders.
• Engage directly with shop-floor personnel for requirements, data validation, and implementation.

Benefits

• Competitive salary, equity plan, and discretionary results-based incentive bonus opportunities.
• Paid vacation, sick time, company holidays, and a year-end shutdown.
• Paid parental leave.
• 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