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Weekend Sas Jobs in Spring, TX (NOW HIRING)

San Jacinto College

Data Scientist I

Pasadena, TX · On-site

Skill in using statistical, programming, database, or analytical tools such as R, Python, SQL, SAS, or comparable platforms to clean, analyze, model, and document data. * Skill in validating ...

San Jacinto College

Data Analyst I

Pasadena, TX · On-site

Access, query, join, and prepare structured data from approved institutional data sources, semantic models, or reporting schemas using tools such as SQL, Python, SAS, Power BI, or comparable ...

Munich Re

Data Scientist, AI

Houston, TX · On-site +1

Proficiency in statistical programming languages such as R, Python, SAS, or similar, alongside general programming and SQL knowledge. * Demonstrated experience building and deploying statistical and ...

Corva

Frac Data Analyst

Houston, TX · On-site

Ability to work nights and weekends in a rotation (Schedule is 7 days on/7 days off, 12 hour shifts, alternate nights/days) * Ability to enter accurate data and prioritize workload * Excellent ...

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How much do weekend sas jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for weekend sas in Spring, TX is $43.61, according to ZipRecruiter salary data. Most workers in this role earn between $30.58 and $54.76 per hour, depending on experience, location, and employer.

What are common challenges faced by weekend SAS programmers, and how can they manage their workload?

Weekend SAS programmers often work on tight deadlines and may have limited access to team support during weekend shifts. Managing time efficiently and proactively communicating with weekday teams about ongoing projects and potential issues are crucial. Additionally, weekend roles may require troubleshooting unexpected data anomalies or system issues independently, so strong problem-solving skills and familiarity with support documentation are essential. Building a clear handover process and maintaining up-to-date project documentation can help ensure smooth collaboration across shifts.

What skills and qualifications are needed to thrive as a weekend SAS analyst?

To thrive as a SAS Analyst, you need a solid background in statistics, data analysis, and programming, often supported by a degree in a quantitative field. Proficiency with SAS software, familiarity with databases, and relevant certifications such as SAS Certified Specialist are typically required. Strong problem-solving skills, attention to detail, and effective time management help you excel, especially when working independently on weekends. These skills ensure accurate data insights, timely project delivery, and high-quality analyses that drive business decisions.

What is a weekend SAS?

Weekend SAs, or Weekend Service Assistants, are employees who typically work part-time shifts on weekends to provide support services in various settings, such as retail, hospitality, healthcare, or customer service. Their responsibilities often include assisting customers, handling basic administrative tasks, and ensuring smooth operations during busy weekend hours. Weekend SAs are essential for organizations that experience increased activity on weekends and need additional staff to maintain service quality and efficiency.
Weekend Sas jobs near you

Data Scientist I

San Jacinto College

Pasadena, TX • On-site

Full-time

Posted 21 days ago

San Jacinto College rating

8.9

Company rating: 8.9 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

34th of 616 rated colleges and universities

Job description

Data Scientist I - District Office
PRIMARY FUNCTION:
The Data Scientist I supports the College's Institutional Research and Data Science function by preparing data, conducting defined components of advanced quantitative analyses, and contributing to statistical, predictive, forecasting, segmentation, and scenario-modeling work. The position applies established analytical methods and Responsible AI procedures under guidance to support institutional research, student success, planning, continuous improvement, and informed decision making. The position assists with evaluating model performance, interpretability, stability, potential bias and fairness risk, privacy implications, limitations, appropriate use, and required human review; documents findings; and escalates complex methodological, ethical, or use concerns. This work helps the College better understand patterns, trends, risk factors, outcomes, and opportunities for improvement through rigorous, transparent, and responsible use of institutional data.
Essential Job Functions:
  • Prepare, clean, transform, and structure institutional, operational, academic, student success, workforce, and other relevant datasets for analytical and modeling use.
  • Conduct quantitative analyses and exploratory data analysis to identify patterns, trends, relationships, and potential explanatory factors that support institutional decision making.
  • Support the development, testing, refinement, and maintenance of statistical, predictive, forecasting, classification, clustering, segmentation, simulation, or other analytical models using approved methods, documented intended uses, and established Responsible AI and human-review procedures.
  • Validate analytical outputs, test assumptions, review results for accuracy, and assist with evaluating model performance, calibration, stability, interpretability, potential bias and fairness risk, privacy implications, limitations, fitness for use, and required human review; document concerns and escalate complex issues appropriately.
  • Create and maintain reproducible code, variable definitions, methodological records, evaluation results, intended-use and limitation statements, model-card components, monitoring information, and escalation records for assigned analytical tasks and recurring models, using established standards and seeking guidance on more complex methodological or Responsible AI issues.
  • Assist with scenario analyses, projections, sensitivity testing, and other applied analytical work that supports planning, evaluation, institutional effectiveness, and continuous improvement.
  • Translate analytical findings into clear summaries, visuals, and presentations for technical and nontechnical audiences, including appropriate explanation of assumptions, uncertainty, limitations, potential bias or fairness concerns, human-review requirements, appropriate use, and practical implications.
  • Collaborate with Data Analysts, Data Pipeline Engineers, AI Application Developers, subject-matter experts, governance partners, and business stakeholders to understand institutional questions, frame analyses, interpret results, support responsible use of advanced analytics, document evaluation concerns, and route data, model, application, privacy, or governance issues to the responsible role.

Additional Job Functions:
  • Participate in professional development related to applied statistics, machine learning, forecasting, model validation, responsible analytics, institutional research, and emerging tools relevant to applied data science.
  • Serve on councils, committees, task forces, or other workgroups as requested to provide institutional research expertise and support for college-wide projects or initiatives. Other duties as assigned.

Knowledge, Skills and Abilities:
  • Knowledgeable of and committed to the philosophy of a comprehensive community college, the College's values and institutional goals, and student success.
  • Knowledge of data security practices and policies, including FERPA, necessary to protect sensitive or confidential information from intentional or unintentional disclosure.
  • Knowledge of applied statistical methods, predictive analytics, forecasting, segmentation, classification, exploratory data analysis, quantitative research methods, and foundational Responsible AI concepts, including interpretability, bias and fairness risk, privacy, human oversight, appropriate use, and escalation.
  • Knowledge of data preparation, feature construction, modeling dataset design, and reproducible analytical workflows.
  • Skill in using statistical, programming, database, or analytical tools such as R, Python, SQL, SAS, or comparable platforms to clean, analyze, model, and document data.
  • Skill in validating analytical results, testing assumptions, reviewing outputs for accuracy, and evaluating model performance, calibration, stability, interpretability, potential bias and fairness risk, limitations, privacy implications, and fitness for use under guidance.
  • Skill in translating analytical findings, limitations, assumptions, and practical implications into clear summaries, visuals, and presentations.
  • Ability to learn institutional data structures, business processes, and decision contexts across academic, student success, workforce, and operational areas.
  • Ability to apply established analytical and Responsible AI evaluation methods accurately under guidance; preserve required human judgment; document findings and limitations; and escalate complex methodological, interpretive, ethical, privacy, or use questions appropriately.
  • Ability to document variable definitions, methods, assumptions, limitations, code, workflows, evaluation results, intended use, human-review requirements, potential risks, and escalation actions in a clear and reproducible manner.
  • Ability to collaborate with analysts, engineers, subject matter experts, and business stakeholders to frame analytical questions and interpret results appropriately.
  • Ability to promote responsible use of advanced analytics through transparency, interpretability, explainability appropriate to the method, validation, privacy awareness, bias and fairness risk review, human oversight, documentation, monitoring, and appropriate caution.
  • Ability to innovate and to think critically to identify prospective improvements to processes across areas of responsibility.
  • Strong written and verbal communication skills to communicate with a broad range of technical and non-technical stakeholders including but not limited to presenting findings and actionable recommendations, writing technical training documents and manuals, and contributing to effective presentations for training and other purposes.
  • Skill and ability to work collaboratively in an open, hybrid, on-site and remote office environment and to adapt to change that requires continuous learning, initiative, and problem-solving.
  • Ability to work independently with attention to details, accuracy, and organization while prioritizing and coordinating multiple, varied projects with effective time and task management involving multiple stakeholders.

Required Education:
  • Bachelor's degree in statistics, data science, economics, mathematics, computer science, quantitative social science, biostatistics, operations research, engineering, or a closely related field.

Preferred Education:
  • Master's degree in statistics, data science, economics, mathematics, computer science, quantitative social science, biostatistics, operations research, engineering, or a closely related field.
  • Graduate coursework in applied statistics, data science, research methods, predictive analytics, machine learning, forecasting, or quantitative methods.

Required Experience:
  • One year of experience in data science, applied statistics, institutional research, quantitative analysis, predictive analytics, or a related field involving structured data, quantitative analysis, analytical documentation, communication of findings, and application of established validation, responsible-use, privacy, or human-review procedures.

OR
  • Documented evidence of comparable capability through relevant graduate work, internships, applied project work, portfolio evidence, professional accomplishments, certifications, applied training, or demonstrated proficiency in preparing data, applying quantitative methods, developing reproducible analyses, documenting methods and limitations, evaluating analytical outputs under established Responsible AI procedures, and communicating findings.

Preferred Experience:
  • Experience performing applied analytical, institutional research, or decision support work in higher education, community college, public sector, or similarly complex mission-driven organizations.
  • Experience supporting student success, enrollment, academic, workforce, operational, or institutional effectiveness analytics in a higher education or comparable institutional setting.
  • Experience working in an enterprise institutional data environment that integrates student, academic, operational, workforce, or other administrative data.
  • Experience supporting applied predictive modeling, forecasting, segmentation, scenario analysis, or similar analytical projects in an institutional or public sector decision support context.

Preferred Licenses/Certifications:
  • Training, coursework, or certification in applied statistics, machine learning, forecasting, data science, analytics, business intelligence, database querying, cloud data platforms, model validation, experimentation, Responsible AI, bias and fairness evaluation, model governance, or comparable areas relevant to applied institutional data science.

Note: This position has limited opportunity for remote work arrangements with appropriate approvals and in accordance with the policies, procedures, and needs of the College.
Salary Grade: 119
Salary is based on the Board-approved salary schedule for the current fiscal year. See Salary Schedule
Requisition Number: req6357
Posting Close Date: 8/7/2026

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