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Quantitative Data Engineer Jobs in Lawrence, KS (NOW HIRING)

micro1 AI

Director of Biostatistics

Olathe, KS · Remote

$60 - $65/hr

... missing data handling in alignment with the statistical analysis plan (SAP). * Identify ... Advanced degree (MSc or PhD) in Biostatistics, Statistics, or a closely related quantitative field.

micro1 AI

Director of Biostatistics

Kansas City, KS · Remote

$60 - $65/hr

... missing data handling in alignment with the statistical analysis plan (SAP). * Identify ... Advanced degree (MSc or PhD) in Biostatistics, Statistics, or a closely related quantitative field.

micro1 AI

Director of Biostatistics

Topeka, KS · Remote

$60 - $65/hr

... missing data handling in alignment with the statistical analysis plan (SAP). * Identify ... Advanced degree (MSc or PhD) in Biostatistics, Statistics, or a closely related quantitative field.

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Showing results 21-29

All JobsQuantitative Data Engineer JobsQuantitative Data Engineer Jobs in Lawrence, KS

Quantitative Data Engineer information

See Lawrence, KS salary details

$10.4K

$122.3K

$186.7K

How much do quantitative data engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for quantitative data engineer in Lawrence, KS is $122,283.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,900.00 and $130,600.00 per year, depending on experience, location, and employer.

What is a quantitative data engineer?

A Quantitative Data Engineer is a professional who designs, builds, and maintains data infrastructure that supports quantitative analysis, typically in finance or technology sectors. They work closely with quantitative analysts and data scientists to ensure efficient data pipelines, data quality, and high-performance systems for processing large datasets. Their responsibilities include developing ETL processes, optimizing databases, and implementing data models to support research and trading strategies. Strong programming skills, expertise in big data technologies, and knowledge of quantitative methods are essential for this role.

How does a quantitative data engineer typically collaborate with data scientists and quantitative analysts on projects?

Quantitative Data Engineers work closely with data scientists and quantitative analysts to design, build, and optimize data pipelines that support complex modeling and analytics. They are often responsible for ensuring data quality, scalability, and efficient data processing, enabling analysts to focus on developing models and extracting insights. Regular collaboration includes translating analytical requirements into technical solutions, troubleshooting data issues, and iterating on data infrastructure to support evolving project needs. This teamwork fosters an environment where technical and analytical expertise complement each other, leading to more robust and actionable results.

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

To excel as a Quantitative Data Engineer, you need strong proficiency in programming (such as Python, R, or C++), advanced mathematical and statistical knowledge, and a relevant degree in computer science, mathematics, or a related field. Experience with big data tools (like Spark, Hadoop), cloud platforms, and data pipeline systems, as well as familiarity with financial data sets, is typically required. Analytical thinking, detail orientation, and effective problem-solving skills distinguish top performers in this role. These competencies are critical for efficiently transforming complex data into actionable insights and supporting robust quantitative models in data-driven environments.

What is the difference between Quantitative Data Engineer vs Data Scientist?

AspectQuantitative Data EngineerData Scientist
Primary FocusBuilding data pipelines, data infrastructure, and ensuring data qualityAnalyzing data, creating models, and deriving insights
Skills & ToolsSQL, Python, Spark, ETL processes, data architectureStatistics, machine learning, Python/R, data visualization
CredentialsComputer science, engineering, or related degrees; certifications in data engineeringStatistics, data science, or related degrees; certifications in data analysis or machine learning
Work EnvironmentData engineering teams, data infrastructure projectsData analysis teams, research, and modeling projects

While both roles work closely with data, Quantitative Data Engineers focus on building and maintaining data systems, whereas Data Scientists analyze data to generate insights and models. They often collaborate but have distinct skill sets and responsibilities within data-driven organizations.

What are popular job titles related to Quantitative Data Engineer jobs in Lawrence, KS?

For Quantitative Data Engineer jobs in Lawrence, KS, the most frequently searched job titles are:

What job categories do people searching Quantitative Data Engineer jobs in Lawrence, KS look for?

The top searched job categories for Quantitative Data Engineer jobs in Lawrence, KS are:

What cities near Lawrence, KS are hiring for Quantitative Data Engineer jobs?

Cities near Lawrence, KS with the most Quantitative Data Engineer job openings:

Quantitative Data Engineer jobs near you
Infographic showing various Quantitative Data Engineer job openings in Lawrence, KS as of June 2026, with employment types broken down into 1% Internship, 95% Full Time, 1% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,283 per year, or $58.8 per hour.

Director of Biostatistics

micro1 AI

Olathe, KS • Remote

$60 - $65/hr

Part-time

Posted 19 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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