Data Scientist I

San Jacinto•Pasadena, TX

4d•Hybrid

About The Position

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.

Requirements

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.
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.

Nice To Haves

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.
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.

Responsibilities

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.
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.