Junior Data Scientist - DA202710

JOB SUMMARY

The Junior Data Scientist supports the Office of the Vice President for Institutional Effectiveness and Planning, particularly the Institutional Research and Data Analytics function, by assisting in the preparation, cleaning, validation, documentation, and analysis of institutional data. The incumbent contributes to the development of reliable datasets, dashboards, KPI monitoring tools, survey analysis, accreditation-related evidence, and standard data requests. Working under the guidance of the Director of Institutional Research and Data Analytics and in collaboration with the Data Scientist and other members of the office, the Junior Data Scientist helps strengthen data quality, reporting efficiency, and the progressive use of analytics, automation, and AI-supported tools for institutional decision-making.

RESPONSIBILITIES AND TASKS

1. Elaborating an internal and external ranking Development Plan:

1. Assists in collecting, extracting, cleaning, and structuring institutional data from academic and administrative sources, such as SIS, LMS, surveys, HR, finance, and other internal systems.
2. Supports data validation activities by checking accuracy, completeness, consistency, duplication, and alignment across sources.
3. Prepares clean datasets, tables, and data extracts to support institutional reporting, dashboards, accreditation requirements, strategic planning, and performance monitoring.
4. Helps maintain organized data files, repositories, and working datasets in line with agreed naming conventions, data definitions, and office procedures.

2. Reporting, Dashboards, and Institutional Analytics Support:

1. Contributes to the development, update, and quality control of dashboards, KPI trackers, survey outputs, and recurring analytical reports.
2. Prepares descriptive statistics, visualizations, summaries, and preliminary analytical insights to support internal requests and evidence-based decision-making.
3. Supports the analysis of student, academic, enrolment, assessment, survey, accreditation, and performance-related data as needed by the Office.
4. Assists in responding to standard data requests by preparing accurate, well-documented, and validated outputs under supervision.

3. Documentation, Data Quality, and Process Improvement:

1. Documents data sources, definitions, transformation steps, assumptions, and quality checks to support transparency and long-term data usability.
2. Identifies discrepancies, missing values, or data integrity issues and escalates them to the Director of Institutional Research and Data Analytics or the Data Scientist for review.
3. Contributes to the continuous improvement of data workflows, reporting templates, and automation routines to increase efficiency and reduce manual work.
4. Respects confidentiality, data governance principles, and institutional policies when handling sensitive academic, student, staff, or institutional data.

4. Applied Analytics, Automation, and AI-Supported Work:

1. Applies basic statistical, machine learning, and data science techniques, where appropriate, to support exploratory analysis, forecasting, classification, or pattern detection under supervision.
2. Uses Python, SQL, spreadsheets, and relevant analytics tools to automate repetitive data preparation and reporting tasks.
3. Explores and supports the responsible use of AI-assisted tools for data cleaning, documentation, text analysis, visualization, and analytical workflow enhancement.
4. Remains updated on relevant data science, analytics, and AI practices that can support the office’s evolving data needs.

KNOWLEDGE AND SKILLS

1. Prior exposure to higher education data, institutional reporting, survey analysis, or accreditation-related evidence is a plus.
2. Working knowledge of data cleaning, data validation, exploratory data analysis, descriptive statistics, and data visualization.
3. Working knowledge of SQL and relational databases; familiarity with data modeling, ETL logic, or data warehousing concepts is an asset.
4. Working knowledge of Python and common data science libraries such as pandas, NumPy, scikit-learn, matplotlib, or equivalent tools.
5. Basic understanding of machine learning concepts, model evaluation, and responsible use of AI-supported tools.
6. Familiarity with dashboarding and reporting tools, preferably Power BI or equivalent platforms.
7. Understanding of data confidentiality, data quality, documentation, and institutional data governance principles.
8. Knowledge of higher education indicators, KPIs, accreditation evidence, survey data, or student success analytics is a plus.
9. Proficiency in Microsoft Excel and good working knowledge of SQL.
10. Ability to prepare clean datasets, perform quality checks, and produce clear tables, charts, and summaries.
11. Ability to use or learn Power BI and other reporting/dashboarding tools.
12. Familiarity with Jupyter, Git, database tools, and basic machine learning libraries is an asset.
13. Strong attention to detail and ability to deal with structured and semi-structured data.

EDUCATIONAL BACKGROUND AND EXPERIENCE

1. Bachelor’s degree in data science, computer science, statistics, information systems, business analytics, or a related field. Master’s degree is a plus.
2. Relevant coursework or applied projects in machine learning, data mining, big data, database design, web applications, or analytics is an asset.
3. 1-2 years of relevant experience in data analysis, data science, institutional research, business intelligence, or a related technical field.
4. Internship, academic, or project-based experience in data cleaning, analytics, machine learning, dashboards, databases, or automation may be considered.

Candidates must meet all employment requirements including, but not limited to, Education and Certification Requirements, Teaching Experience, Reference Checks and Criminal Background Checks.

Additional Details about this position will only be provided to Shortlisted Candidates.