Responsibilities
1.
Data Quality
Ensure accuracy, completeness, and consistency of data across multiple systems.
Uphold data governance standards, managing access to sensitive information appropriately.
2.
Data Analysis & Modeling
Analyze diverse data sources (e.g., surveys, sensors, attendance metrics) to identify trends, patterns, and insights.
Perform Exploratory Data Analysis (EDA) and feature engineering to prepare datasets for deeper analysis.
Contribute to the design, implementation, and validation of predictive models using machine learning and statistical techniques (e.g., regression, classification).
Write SQL queries to extract and manipulate data from relational databases.
Learn and apply new analytical methods to enhance model performance and generate actionable insights.
Use Git/GitHub for version control and collaborative development.
3.
Reporting & Communication
Develop reports and visualizations to clearly communicate insights to stakeholders.
Build, maintain, and share dashboards and reports using tools such as Tableau, R Shiny, ThoughtSpot, or G Suite.
Present findings and recommendations to both client and internal audiences.
Requirements
Core Qualifications
Bachelor s degree in Statistics, Computer Science, Data Science, Psychology, or related analytics-focused field, OR 1+ years of professional experience in an analytical role.
Motivated self-starter with the ability to work proactively with guidance.
Strong organizational skills and attention to detail.
Proficiency with Microsoft Office and Google Suite.
Clear and effective communication skills (written and verbal).
Foundational understanding of analytics and data quality principles.
Preferred Qualifications
Advanced degree in a quantitative or related field.
Experience conducting research and performing deep analysis on data sets for insights.
Programming skills in R or Python for data analysis.
Experience creating dashboards or visualizations with Tableau and/or ThoughtSpot.
Academic or project-based experience applying core Machine Learning algorithms (e.g., Linear Regression, Logistic Regression, Decision Trees) with libraries like scikit-learn.
Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and SQL querying.
Knowledge of architecture and building standards (a plus, but not required).