Job title:DATA SCIENCE TEAM

School/Function:Multi-Function

Location:London

Responsible to:Data Scientist

Overall purpose

AI Regent Data Science team will play a crucial role in improving institutional decision-making, enhancing student outcomes, optimizing resources, and supporting research initiatives. The team typically will collaborate with various departments such as Admissions, Academic Operations, Student Experience, Academic Research, and IT, providing data-driven insights that will guide Regent strategies, enhance learning experiences, and improve operational efficiency.

At AI Regent we are committed to forming a new Data Science team that will describe Data Science as being a five-part cycle of information:

Process: Data mined, modelled and summarized

Analyse: Conduct predictive analysis, regressions, and quantitative analysis

Communicate: Data reporting and visualization.

Data Capture: Data acquisition and entry, signal reception and data extraction,

Maintenance: Cleanse, process and warehouse of data.

As part of forming the Data Science Team we are looking for:

Data Engineer:responsible for the infrastructure and systems that ensure data is collected, cleaned, and made accessible for analysis. Manage the data pipelines, databases, and storage systems necessary for data scientists to do their work.

Skills:SQL, Python, ETL tools, cloud platforms (AWS, Google Cloud), database management (e.g., PostgreSQL, MySQL, NoSQL), Hadoop, Spark.

Academic Data Analyst:focus on using data to improve academic performance and student learning outcomes. Will work closely with Academics to conduct analyses that support curriculum development and student interventions.

Skills: Excel, SQL, basic statistical analysis, data visualization, survey design.

Student Success Analyst:uses data to help improve retention and graduation rates, often focusing on early intervention systems and personalized student support.

Skills: Statistical analysis, predictive modelling, academic performance data, CRM systems.

Institutional Research Analyst:work with a broad range of institutional data to support accreditation efforts, strategic planning, and compliance with federal and state reporting requirements. Their role often includes working with external surveys, rankings, and benchmarking.

Skills: Statistical analysis, SQL, survey methodology, report writing, data visualization, knowledge of regulatory requirements.

Data Science Manager or Lead:leads the team, sets strategic goals, and ensures alignment with the university's mission. They also manage relationships with key stakeholders across departments (e.g., academic operations, registry and attendance, IT, etc.).Skills: Leadership, project management, strategic thinking, advanced analytics, communication

Specific duties and responsibilities

Student Retention and Success:

• Predictive Modelling: Data science teams build predictive models to identify students at risk of academic failure or dropout, allowing institutions to intervene early with targeted support programs (e.g., academic advising, counselling, tutoring).
• Personalized Learning: Developing adaptive learning systems or personalized learning paths based on student performance and behaviour data, enhancing student engagement and success.

Enrolment Management and Admissions:

• Enrolment Forecasting: Data scientists use historical data and demographic trends to predict future enrolment, helping universities plan for the right level of resources and staffing.
• Admissions Analytics: Analysing the factors that predict student success based on admissions data (e.g., test scores, high school GPA, socioeconomic background) to refine admissions strategies and scholarship offerings.

Curriculum Optimization:

• Course Performance Analysis: Analysing course-level data (e.g., pass rates, GPA distributions, student feedback) to identify bottlenecks in the curriculum and recommend improvements or changes to course structures.
• Curriculum Effectiveness: Evaluating the success of new academic programs, teaching methods, and course designs based on student outcomes data.

Operational Efficiency:

• Resource Allocation: Data science teams work with administrative units to optimize the allocation of resources such as classroom space, faculty assignments, and class schedules, improving cost efficiency.
• Budget Forecasting: Predictive analytics can be used to forecast budgeting needs based on enrolment trends, student demand, and faculty requirements.

Research and Academic Analytics:

• Research Performance: Supporting faculty and researchers by analysing research output (publications, grants, citations) and identifying trends in academic research.
• Benchmarking: Analysing data from external sources (e.g., rankings, peer institutions) to benchmark the university’s performance in areas like faculty productivity, student success, and research impact.

Diversity, Equity, and Inclusion (DEI):

• DEI Initiatives: Analysing demographic data to evaluate the university's progress toward diversity, equity, and inclusion goals. Identifying patterns or disparities in enrolment, retention, and graduation rates among different demographic groups.
• Access to Education: Supporting initiatives aimed at improving access to higher education for underserved populations by analysing data on financial aid, scholarships, and first-generation college students.

To Apply:Please submit your resume, cover letter, and any relevant project portfolios or GitHub repositories to mentioning in the SUBJECT line which particular role you will be best adapted to work in.

1. Data Engineer
2. Academic Data Analyst
3. Student Success Analyst
4. Institutional Research Analyst
5. Data Science Manager or Lead