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Sas Predictive Modeling Jobs (NOW HIRING)

Aegis Corps, Inc.

Risk Analyst

Washington, DC

We are seeking an experienced predictive risk modeler to perform risk assessment on FHA multifamily ... Perform all analytical and modelling work in SAS. * Recommend ways to streamline work processes ...

TecTammina

Statistical Modeler

San Antonio, TX · On-site

$49.50 - $64/hr

Strong background in statistical modeling and experience with statistical tools such SAS Enterprise Guide, SAS Enterprise Miner, SASS or SPSS. Working with other statisticians to build predictive ...

Regional Finance

Sr Credit Risk Analyst

Plano, TX · On-site

Duties and responsibilities • Mines, models, analyzes large datasets, and utilizes predictive ... SAS and ultimately delivers a final insightful recommendations to stakeholders. Minimum ...

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How much do sas predictive modeling jobs pay per hour?

As of May 30, 2026, the average hourly pay for sas predictive modeling in the United States is $54.12, according to ZipRecruiter salary data. Most workers in this role earn between $50.48 and $59.86 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a SAS Predictive Modeling Specialist, and why are they important?

To thrive as a SAS Predictive Modeling Specialist, you need a solid background in statistics, data analysis, and predictive modeling, typically supported by a degree in mathematics, statistics, or a related field. Proficiency in SAS software (such as SAS Enterprise Miner), familiarity with databases, and relevant certifications like SAS Certified Predictive Modeler are highly beneficial. Strong problem-solving skills, attention to detail, and effective communication help translate complex data insights into actionable business strategies. These skills ensure accurate model development and interpretation, driving data-driven decision-making and business success.

What are some common challenges faced when implementing SAS predictive modeling solutions in a business setting?

One common challenge in SAS predictive modeling roles is ensuring data quality and consistency across large, complex datasets, which can significantly impact model accuracy. Another frequent obstacle is effectively communicating technical findings to non-technical stakeholders, making it crucial to translate predictive insights into actionable business recommendations. Additionally, integrating SAS models into existing business processes or IT systems can require close collaboration with IT teams and may involve troubleshooting compatibility or deployment issues. Successful candidates are often those who can balance technical expertise with strong problem-solving and communication skills.

What is SAS predictive modeling?

SAS predictive modeling refers to the use of SAS (Statistical Analysis System) software to create statistical models that forecast future outcomes based on historical data. These models help organizations identify patterns, trends, and relationships within data to make informed decisions. SAS offers a suite of powerful analytical tools and procedures for tasks such as regression, classification, forecasting, and machine learning. Predictive modeling with SAS is widely used in industries like finance, healthcare, marketing, and insurance for risk assessment, customer segmentation, and more.

What is the difference between Sas Predictive Modeling vs Data Scientist?

AspectSas Predictive ModelingData Scientist
Required CredentialsTypically requires SAS certifications, statistical or data analysis degreesOften requires degrees in computer science, statistics, or related fields; certifications like SAS or Python are common
Work EnvironmentPrimarily in analytics or data-focused teams within industries like finance, healthcare, or marketingIn diverse settings including tech, finance, healthcare, with broader responsibilities
Employer & Industry UsageUsed by companies leveraging SAS software for predictive analyticsUsed across industries for data analysis, machine learning, and data-driven decision making

While Sas Predictive Modeling focuses on building predictive models using SAS software, Data Scientists have a broader role involving data analysis, machine learning, and programming in various languages. Both roles require strong statistical skills, but Data Scientists often work with multiple tools and handle more diverse data tasks.

More about Sas Predictive Modeling jobs
What states have the most Sas Predictive Modeling jobs? States with the most job openings for Sas Predictive Modeling jobs include:
What job categories do people searching Sas Predictive Modeling jobs look for? The top searched job categories for Sas Predictive Modeling jobs are:
Sas Predictive Modeling jobs near you
Infographic showing various Sas Predictive Modeling job openings in the United States as of May 2026, with employment types broken down into 44% Full Time, 25% Part Time, 29% Contract, and 2% Nights. Highlights an 59% Physical, 33% Hybrid, and 8% Remote job distribution, with an average salary of $112,568 per year, or $54.1 per hour.

Post-Doctoral Research Associate (Informatics - Center 4 Health Research)

University of Illinois Hospital and Health Sciences System

Peoria, IL • On-site

Other

Posted 16 days ago

Job description

Position Summary
This postdoctoral position will play a critical role indesigning, implementing, and analyzing research projects focused on identifyingand addressing health disparities, with an emphasis on patient-centered digitalhealth interventions (such as Epic MyChart). This role also provides anopportunity to apply advanced informatics, artificial intelligence, human-computerinteraction, and data science methods to understand and mitigate healthdisparities, enhance patient engagement, and improve outcomes through digitalhealth innovations. The successful candidate will collaborate with amultidisciplinary team and contribute to building evidence that informs policyand practice.
Duties & Responsibilities

  • Scholarly Activities: Prepare manuscripts for peer-reviewed journals and present research findings at national and international conferences. Assist in the development of grant proposals to secure funding for current and future research projects.
  • Career Development & Mentorship: Work with the Supervisor to develop a personalized professional growth plan with opportunities for publications, presentations, grant development, and career advancement.
  • Research Design & Implementation: Develop and support mixed-methods research strategies to identify and address health disparities, particularly in rural and urban populations, through patient-centered and community-informed approaches.
  • Survey & Data Collection: Design and implement survey tools such as REDCap or Qualtrics, and collect, manage, and analyze diverse datasets, including EHR (EPIC), surveys, interviews, and population-level data. Qualitative & Quantitative Analysis: Conduct interviews or focus groups, perform statistical and thematic analyses, and apply appropriate models (e.g., regression, ANOVA) to explore outcomes and associations.
  • Digital Health & Informatics: Leverage data from EHRs, patient portals, mobile health apps, and wearables to generate insights, predictive models, and decision-support tools; collaborate with informatics teams to evaluate and optimize digital intervention.
  • AI, Predictive Modeling & Data Visualization: Utilize methods such as machine learning / predictive modeling to identify trends or high-risk groups, and develop interactive dashboards (e.g., Tableau, Power BI, R, SAS) to communicate findings.
  • Open Science & Data Stewardship: Maintain reproducible research practices, ensure data quality and compliance, and uphold IRB and ethical standards throughout project execution.
  • Community Engagement: Collaborate with patients, clinicians, and community stakeholders to ensure research benefits all.
  • Career & Interdisciplinary Engagement: Collaborate closely with patients, clinicians, community partners, and researchers across disciplines (public health, epidemiology, behavioral science, informatics) to ensure equitable impact and effective dissemination.
  • Interdisciplinary Collaboration: Partner with researchers in public health, health services, epidemiology, behavioral sciences, and informatics to guide study design and dissemination.
  • Perform other related duties and participate in special projects as assigned.

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