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Statistical Engineering Jobs in Chicago, IL (NOW HIRING)

micro1 AI

Director of Biostatistics

Elgin, IL · Remote

$60 - $65/hr

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 ...

micro1 AI

Director of Biostatistics

Naperville, IL · Remote

$60 - $65/hr

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 ...

MMS

Study Design Statistician (US)

Chicago, IL · On-site

Provide statistical consultancy support to sponsors across the full span of clinical development, including oversight of third-party statistical and programming deliverables. * Maintain current ...

Sterling Engineering Inc.

Quality Engineer

Barrington, IL

$90K - $110K/yr

Apply statistical methods to collect, analyze, interpret, and summarize production data to improve ... Bachelor's degree in Mechanical Engineering, Industrial Engineering, or related field. * 5-8+ years ...

Kemper

Data Science Manager

Chicago, IL · Hybrid

PhD in Mathematics, Statistics, Engineering, or other STEM fields preferred with 4-6 years of experience in insurance industry or related industry in a data science, analytics, or AI environment.

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All JobsStatistical Engineering JobsStatistical Engineering Jobs in Chicago, IL

Statistical Engineering information

See Chicago, IL salary details

$62.3K

$73.1K

$81.5K

How much do statistical engineering jobs pay per year?

As of Aug 9, 2026, the average yearly pay for statistical engineering in Chicago, IL is $73,058.00, according to ZipRecruiter salary data. Most workers in this role earn between $67,800.00 and $78,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a statistical engineer, and why are they important?

To thrive as a Statistical Engineer, you need strong quantitative analysis skills, a background in statistics or mathematics, and often a relevant degree such as in engineering or applied statistics. Proficiency with statistical software (e.g., R, SAS, Python), data management systems, and sometimes Six Sigma certification is typically required. Critical thinking, problem-solving, and clear communication are crucial soft skills for interpreting data and collaborating with multidisciplinary teams. These skills ensure accurate data-driven decisions, efficient process improvements, and effective solutions to complex engineering challenges.

What is the difference between Statistical Engineering vs Data Scientist?

AspectStatistical EngineeringData Scientist
Required credentialsStatistics, Data Analysis, EngineeringStatistics, Computer Science, Data Analysis
Work environmentManufacturing, R&D, Engineering teamsBusiness, Tech, Research sectors
Employer usageOptimizing processes, designing experimentsBuilding models, insights, predictive analytics

Statistical Engineering focuses on applying statistical methods to improve engineering processes and product development, often within manufacturing or R&D settings. Data Scientists analyze large datasets to extract insights, build predictive models, and support business decisions. While both roles require strong statistical skills, Statistical Engineering emphasizes process optimization and experimental design, whereas Data Scientists focus on data-driven insights across diverse industries.

How does a statistical engineer typically collaborate with cross-functional teams to implement data-driven solutions?

Statistical Engineers frequently work alongside data scientists, software engineers, and business analysts to design and implement robust data-driven solutions. They are responsible for translating complex statistical models into actionable insights and ensuring that these models are integrated effectively within existing systems. Collaboration often involves regular meetings to align on project goals, sharing progress updates, and troubleshooting technical challenges together. This interdisciplinary teamwork is essential for ensuring that statistical methodologies are not only theoretically sound but also practically applicable to real-world business problems.

What is statistical engineering?

Statistical engineering is an interdisciplinary field that focuses on the integration and application of statistical methods and principles to solve complex, large-scale problems in science, business, and engineering. It involves designing data collection processes, analyzing and interpreting data, and implementing statistical solutions within larger systems. Statistical engineers often work on projects that require collaboration with other engineering disciplines, using statistics as a foundational tool to drive decision-making and innovation.
What are popular job titles related to Statistical Engineering jobs in Chicago, IL? For Statistical Engineering jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Statistical Engineering jobs in Chicago, IL look for? The top searched job categories for Statistical Engineering jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Statistical Engineering jobs? Cities near Chicago, IL with the most Statistical Engineering job openings:
Statistical Engineering jobs near you
Infographic showing various Statistical Engineering job openings in Chicago, IL as of July 2026, with employment types broken down into 95% Full Time, 3% Part Time, and 2% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $73,058 per year, or $35.1 per hour.

Director of Biostatistics

micro1 AI

Elgin, IL • Remote

$60 - $65/hr

Part-time

Posted 6 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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