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Decision Support Data Scientist

FLSA Status: Exempt

Months Per Year: 12

Employment Status: Full-Time

Work Model: Hybrid Available

Seattle University will be one of the most innovative and progressive Jesuit and Catholic universities in the world, educating with excellence at the undergraduate, graduate, and professional levels. We embrace an intersectional framework for defining diversity in its broadest sense, including differences in gender, gender identity, race, ethnicity, generational history, culture, socioeconomic class, religion, sexual orientation, national origin, citizenship status, political perspectives, geographic origin and physical ability. Seattle University strives to be a welcome, open and safe campus climate for all who learn, live and work at Seattle University.

Position Description

Overview: The Data Scientist will play a crucial role in advancing the university's data-informed decision-making processes by applying advanced statistical, analytical, and machine learning techniques to complex institutional data. This role will be responsible for technical development and deployment end-to-end analytical lifecycles. The Data Scientist will develop, improve, and maintain data models, code, and performance of production level data models to ensure timely, accurate, and sustainable analyses. The Data Scientist plays a collaborative role through the continuous improvement of data ingestion, standardization, reporting and interpretation of predictive and descriptive analytics.

Data Analytics Life Cycle

Model Design: Translate business requirements and stakeholder requests into technical requirements.

  • Evaluate technical requirements to identify data requirements and/ or gaps.
  • Evaluate the appropriateness of analytical approaches and advise on best practices for model identification in coordination with the Lead Data Scientist.
  • Collaborate with data stewards, system subject matter experts, and stakeholders to ensure data accuracy and identify data ingestion pipelines and improvement.
  • Develop subject matter expertise for critical or novel data models or systems.
    Automate and integrate ad hoc analyses.

Data Engineering: Develop initial analytical models to extend or enhance production models. Independently or in partnership with the BI Engineer, create data pipelines for data ingestion.

  • Transform data for model ingestion using available frameworks such as AWS, CSV files, and databases.
  • Profile data to explore and validate data content, structure, and establish error detection functions.
  • Support strategic initiatives and special projects through continuous improvement and automation of prototype and production data models and analyses.

Model Engineering: Execute algorithms and processes to obtain key findings.

  • Develop standard procedures to write, train, and test appropriate analytical or machine learning models including feature engineering and hyperparameter tuning.
  • Collaborate with Lead Data Scientist to ensure the model meets original objectives and address any questions about data and methodological validity.
  • Document metadata, transformations, and pipeline processes.

Analytical Operations: Deploy analytical models into production environment.

  • Prepare the model artifact for production environment.
  • Ensure accurate version control of code and data processes ensuring that modification or enhancements are through continuous improvement/ continuous development pipelines.
  • Establish and automate model performance monitoring and re-training triggers.
  • Provide training and ongoing support to users of analytical products.

Analytical Findings: Provide timely interpretation and validation of predictive models to stakeholders.

  • Design and create data visualizations, dashboards, scorecards, and reports to effectively communicate complex insight from models to university stakeholders.
  • Collaborate with Lead Data Scientist to ensure findings meet statistical and internal standards.
  • Collaborate with BI developer to integrate data or findings into existing reporting or new reports.
  • Collaborate with the Director of Decision Support to ensure stakeholders have sufficient understanding of methodology and findings to guide decision making process.
  • Relationship Management: establishing and maintaining relationships across the team and cross-functionally.
  • Management of competing priorities and expectations across different teams and stakeholders.
  • Effective communication and collaboration skills, especially in working with cross-functional teams, faculty and staff across diverse institutional research initiatives.
  • Collaborate across the Decision Support team and cross-functionally to solicit feedback and continuously improve our operations and analytical products.
  • Collaborate with campus users to improve survey design, administration, and analysis.
  • Act as campus Qualtrics brand administrator.
  • Stay up to date with best practices and emerging trends in data science, institutional research, ML operations and higher education, and recommend changes to analytical methodologies and tools as needed to ensure the university remains competitive and effective.

Qualifications

Required Minimum Qualifications 

  • Advanced understanding of data analysis including descriptive statistics, predictive and explanatory statistical modeling techniques, and data mining or embedded analytics techniques, typically requiring a graduate degree in statistics or a statistical social science field or equivalent experience.
  • At least five years professional work experience in higher education. 
  • Knowledge of the data sources, metrics, and analysis best practices in higher education. 
  • Ability to lead and collaborate with faculty and staff across a comprehensive set of institutional research initiatives and projects. 
  • Proficiency programing with Python or R with experience writing efficient and optimized code.
  • Knowledge of machine learning algorithms, including supervised and unsupervised learning techniques such as regression, decision, trees, random forests, clustering algorithms, and neural networks.
  • Understanding of the strengths and limitations of statistical and machine learning algorithms for model selection and optimization.
  • Expertise in data preprocessing techniques, such as data cleaning, normalization, and feature scaling.
  • Proficiency in evaluating and validating machine learning models using appropriate performance metrics, cross-validation techniques, and methods like precision, recall, ROC-AUC, and confusion matrices is crucial. Experience in tuning hyperparameters to optimize model performance is also important.
  • Experience with data warehousing and relational databases including big data processing frameworks like Apache Spark or AWS as well as managing large-scale datasets using SQL or Hadoop.
  • Knowledge of data visualization and executive dashboard development techniques.  
  • Ability to solve problems, work effectively independently and as a part of a team, manage multiple short- and long-range projects simultaneously, and design and conduct institutional research studies. 
  • Ability to interpret and apply federal and state regulations and University policies governing the appropriate use and dissemination of student, employee, and institutional data.  
  • Strong oral and written communication skills. 
  • Proficiency with MS Office software including Excel, Access, Word, and PowerPoint. 
  • Strong attention to detail and accuracy.  
  • Strong interpersonal skills.  
  • Positive attitude and ability to work with individuals from a variety of backgrounds and ability levels within the University. 

Application Instructions

Please attach a cover letter with your resume when applying. Job postings are open until filled, unless otherwise specified. 

Compensation at a Glance:

Salary Range: $82,200 - $123,300

Seattle University has provided a compensation range that represents its good faith estimate of what the University may pay for the position at the time of posting. The salary offered to the selected candidate will be determined based on factors such as the qualifications of the selected candidate, departmental budget availability, internal salary equity considerations, and available market information, and not based on a candidate’s gender or any other protected status.

Your total compensation goes beyond the number on your paycheck. Seattle University provides generous leave, health plans, and retirement contributions that add to your total compensation package.

Benefits at a Glance:

Consistent with its fundamental Jesuit values, Seattle University offers a wide range of benefits designed to care for the whole person. Choose from three different medical plans, a dental, and vision insurance programs. Protect your income with life, short & long-term disability coverage. Plan for your future with up to a 10% employer contribution for retirement benefits, comprised of a 5% nonelective employer contribution and an additional dollar-for-dollar match of your voluntary contributions up to a maximum of 5%. You may also take advantage of 100% paid tuition benefits for the employee and dependents, a subsidized transportation benefit, a wellness program with free access to an onsite fitness facility, and a wide variety of campus events. Enjoy a generous holiday schedule, including a paid Holiday break closure in December, vacation and paid sick leave, and paid community service leave. For more information explore the Benefits website at: https://www.seattleu.edu/hr/benefits/ 

SU
Seattle University
901 12th Avenue
Seattle, WA 98122
206-296-6000