Adjunct Faculty - Data Science

Adjunct Faculty – M.S. in Data Science

University: University of Bay Area (ubayarea.org)
Department: M.S. in Data Science

Position Summary

The University of Bay Area is seeking qualified Adjunct Faculty to teach graduate-level courses in its Master of Science in Data Science program. Adjunct faculty will provide rigorous, applied instruction in areas such as statistical analysis, machine learning, data mining, data visualization, programming for data science, database systems, and applied analytics.

Successful candidates will combine strong subject-matter expertise with the ability to teach graduate students how to apply data science methods to real-world problems. Faculty are expected to deliver instruction consistent with approved course learning outcomes, maintain high academic standards, and support students in developing both technical and analytical competencies required for careers in data science and related fields.

Courses may be delivered in a hybrid or in-person format and may include evening sessions depending on program scheduling.

Key Responsibilities

• Teach assigned graduate-level courses in accordance with the approved syllabus, course objectives, and program learning outcomes.
• Deliver instruction in one or more areas of data science, including:
• Python or R programming for data analysis
• Probability and statistical inference
• Regression and predictive modeling
• Machine learning and supervised/unsupervised learning
• Data mining and exploratory data analysis
• Data visualization and business intelligence
• SQL, relational databases, and data management
• Big data technologies and distributed data processing
• Applied artificial intelligence and model development
• Experimental design, A/B testing, and causal analysis
• Data ethics, privacy, governance, and responsible AI
• Develop and maintain course materials, including lectures, demonstrations, laboratory exercises, programming assignments, case studies, projects, quizzes, examinations, and grading rubrics.
• Incorporate hands-on programming and analytical exercises using appropriate tools and platforms such as Python, R, SQL, Jupyter, Git/GitHub, Tableau, Power BI, or cloud-based data science environments, as appropriate to the course.
• Design assignments and projects that require students to work with real-world or appropriately simulated datasets.
• Teach students to collect, clean, transform, analyze, visualize, and interpret structured and unstructured data.
• Provide instruction on selecting appropriate statistical or machine learning methods, evaluating model performance, interpreting results, and communicating findings to technical and non-technical audiences.
• Provide timely, substantive feedback on assignments, projects, and assessments.
• Hold scheduled virtual or in-person office hours and provide academic guidance to graduate students.
• Evaluate student work using clearly defined and consistently applied grading criteria.
• Submit final grades, attendance records, assessment documentation, and other required academic materials by established deadlines.
• Use the university's learning management system and other approved instructional technologies to support course delivery.
• Coordinate with the Academic Director and other Data Science faculty to maintain consistency in course content, technical requirements, datasets, software, and assessment standards.
• Participate in faculty orientation, curriculum meetings, assessment activities, and professional development sessions as required.
• Monitor student progress and communicate significant academic, attendance, or integrity concerns through established university procedures.
• Maintain current knowledge of developments in data science, machine learning, artificial intelligence, analytics, and related technologies and incorporate relevant industry practices into instruction where appropriate.
• Support a graduate-level learning environment that emphasizes analytical reasoning, reproducible research, ethical use of data, and practical application of data science methods.

Required Qualifications

• Master's degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, Artificial Intelligence, or a closely related field from an accredited institution.
• Demonstrated subject-matter expertise in the specific data science course or courses assigned.
• Strong knowledge of quantitative and analytical methods appropriate to graduate-level data science education.
• Demonstrated proficiency in relevant programming, statistical, database, or analytics tools appropriate to the assigned course.
• Ability to explain advanced technical concepts clearly to students with varying levels of programming, mathematics, and statistics experience.
• Ability to develop and evaluate graduate-level assignments, projects, examinations, and technical assessments.
• Strong written and verbal communication skills.
• Experience using learning management systems and technology-supported or online instruction.
• Ability to teach graduate students in a rigorous, inclusive, and professionally oriented academic environment.

Preferred Qualifications

• Ph.D., D.Sc., or other terminal degree in Data Science, Computer Science, Statistics, Mathematics, Artificial Intelligence, or a related discipline.
• Prior teaching experience in a graduate-level data science, analytics, computer science, statistics, or related program.
• Professional experience in data science, machine learning, analytics, software engineering, data engineering, artificial intelligence, or a related field.
• Experience developing production or research-oriented machine learning models and data pipelines.
• Experience with modern data science technologies and platforms, such as Python, R, SQL, Git, Docker, cloud computing, Spark, Databricks, AWS, Azure, or Google Cloud Platform.
• Experience with data visualization and analytics platforms such as Tableau or Power BI.
• Experience supervising graduate-level data science projects, capstone projects, research projects, or applied analytics work.
• Peer-reviewed publications, conference presentations, applied research, or other scholarly/professional contributions in data science or a related discipline.
• Relevant professional certifications or demonstrated industry expertise in data science, analytics, cloud computing, or machine learning.
• Experience teaching diverse graduate student populations in hybrid or in-person learning environments.

Potential Teaching Areas

Depending on qualifications and program needs, adjunct faculty may be considered for courses such as:

• Foundations of Data Science
• Programming for Data Science
• Statistical Methods for Data Science
• Exploratory Data Analysis and Data Visualization
• Database Systems and SQL for Data Science
• Machine Learning
• Applied Predictive Modeling
• Data Mining
• Deep Learning and Neural Networks
• Natural Language Processing
• Big Data Analytics
• Cloud Computing for Data Science
• Business and Applied Analytics
• Data Engineering
• Experimental Design and A/B Testing
• Responsible AI, Data Ethics, and Privacy
• Data Science Capstone Project

Working Expectations

Adjunct faculty are expected to:

• Maintain consistent communication with students throughout the academic term.
• Meet all scheduled class sessions and instructional obligations.
• Keep course materials, assignments, datasets, and technical demonstrations current and accessible.
• Apply grading and assessment standards consistently across students.
• Protect student data and maintain confidentiality in accordance with university policies and applicable requirements.
• Follow university policies related to academic integrity, accessibility, student conduct, data privacy, and responsible use of artificial intelligence.
• Use university-approved software, systems, datasets, and instructional technologies where required.
• Complete required administrative and faculty development activities within established deadlines.

The University of Bay Area is an equal opportunity employer committed to diversity, equity, and inclusion. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.

Pay: $25.00 - $120.00 per hour

Benefits:

• Flexible schedule

Education:

• Master's (Required)

Experience:

• Data science: 2 years (Preferred)

Ability to Commute:

• San Jose, CA 95128 (Required)

Work Location: Hybrid remote in San Jose, CA 95128