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Process Modeling Engineer Jobs in Wanette, OK (NOW HIRING)

micro1 AI

Director of Biostatistics

Norman, OK · Remote

$60 - $65/hr

Your work will shape how models learn, reason, and perform through high-quality, real-world input ... Establish defensible ground truth for each evaluation task, documenting the derivation process to ...

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Process Modeling Engineer information

See Wanette, OK salary details

$43.4K

$80.7K

$125K

How much do process modeling engineer jobs pay per year?

As of Sep 14, 2026, the average yearly pay for process modeling engineer in Wanette, OK is $80,719.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,400.00 and $90,400.00 per year, depending on experience, location, and employer.

What does a process modeling engineer do?

A Process Modeling Engineer is responsible for designing, analyzing, and optimizing processes within industries such as manufacturing, chemical production, or energy. They use mathematical models and simulation software to predict how processes will perform under various conditions. By creating digital models, they help improve efficiency, reduce costs, and ensure safety and quality standards are met. Their work often involves collaborating with other engineers and stakeholders to implement improvements based on data-driven insights.

What are the key skills and qualifications needed to thrive as a process modeling engineer?

To thrive as a Process Modeling Engineer, you need a strong background in chemical or process engineering, analytical problem-solving skills, and typically a relevant engineering degree. Familiarity with process simulation software such as Aspen Plus, HYSYS, or MATLAB, and understanding of industry standards are crucial for effective modeling and analysis. Strong communication, teamwork, and project management skills help you collaborate with cross-functional teams and convey complex technical information. These skills ensure accurate process optimization, efficient project execution, and drive operational improvements in manufacturing or industrial environments.

What are some typical challenges faced by process modeling engineers when collaborating with cross-functional teams?

Process Modeling Engineers often work closely with teams from operations, R&D, and IT to develop and refine models that optimize manufacturing or chemical processes. A common challenge is translating complex technical data into actionable insights that are easily understood by non-engineering stakeholders. Effective communication and adaptability are key, as project requirements can evolve rapidly and may require balancing competing priorities. Building strong relationships and maintaining open channels for feedback help ensure that process models align with both technical standards and business goals.

What is the difference between Process Modeling Engineer vs Process Improvement Specialist?

AspectProcess Modeling EngineerProcess Improvement Specialist
Required CredentialsBachelor's in Engineering, Industrial Engineering, or related field; proficiency in process modeling softwareBachelor's in Engineering, Business, or related field; certifications like Six Sigma often preferred
Work EnvironmentEngineering teams, manufacturing plants, or R&D labsOperational teams, manufacturing facilities, or corporate offices
Employer & Industry UsageManufacturing, aerospace, automotive, and industrial sectorsManufacturing, healthcare, logistics, and service industries

The Process Modeling Engineer focuses on creating detailed process models using specialized software to optimize workflows. In contrast, the Process Improvement Specialist concentrates on analyzing existing processes and implementing improvements, often utilizing methodologies like Six Sigma. Both roles require similar educational backgrounds but differ in their primary focus and tools used.

Process Modeling Engineer jobs near you

Director of Biostatistics

Norman, OK • Remote

micro1 AI
Software Development • 11 - 50 employees
Learn more about micro1 AIexternal

$60 - $65/hr

Part-time

Re-posted 11 days ago

Job description

Role Title: Biostatistician


Role Type: Contractor


Location: Remote


micro1 is engaging Biostatisticians to contribute their clinical statistics expertise to a dynamic customer project focused on AI-assisted clinical research. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Author and review evaluation tasks that require deriving, reproducing, or validating statistical outputs from clinical datasets and associated tables, figures, and listings (TFLs).
  2. Apply expert judgment to assess the correctness and consistency of reported estimates, confidence intervals, p-values, analysis populations, and missing data handling in alignment with the statistical analysis plan (SAP).
  3. Identify discrepancies between statistical outputs and their narrative descriptions in clinical study reports (CSR), including subtle errors in population definitions, censoring rules, or multiplicity handling.
  4. Establish defensible ground truth for each evaluation task, documenting the derivation process to enable independent verification.
  5. Provide structured written rationales distinguishing true statistical errors from acceptable methodological alternatives, employing clear and concise communication.
  6. Collaborate with a multidisciplinary project team, providing statistical insights and feedback as needed to refine evaluation tasks and criteria.


Preferred Qualifications

  1. 5+ years as a biostatistician supporting clinical trials at a sponsor, CRO, or academic trials unit.
  2. Hands-on experience producing or quality controlling TFLs for regulatory submissions and working directly from CDISC SDTM/ADaM datasets.
  3. Solid understanding of statistical methods used in confirmatory trials, including survival analysis, mixed models, covariate adjustment, multiplicity control, and estimand and missing-data strategies under ICH E9(R1).
  4. Proficiency in SAS and/or R, with the ability to independently reproduce analyses from written specifications.
  5. Ability to interpret SAPs and ensure reported results are consistent with pre-specified analyses.
  6. Advanced degree (MSc or PhD) in Biostatistics, Statistics, or a closely related quantitative field.
  7. Nice to have: Experience as lead statistician on pivotal/registrational studies, authoring or reviewing CSR statistical sections, oncology endpoint expertise, and exposure to AI-assisted statistical review tools.

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