Data Science

Develop and implement data science models to enhance learning, assessment, and training platforms. Conduct data analysis, statistical analysis, and predictive modeling to identify trends and patterns in educational data. Develop machine learning models including logistic regression, random forest, and clustering algorithms tailored for educational purposes. Use psychometric methods like Item Response Theory and Rasch Models to improve assessment data quality. Create data visualizations and dashboards using Power BI and Tableau for educational stakeholders. Work with cross-functional teams, including educators and researchers, to understand requirements and deliver insights. Optimize ETL processes for data integration and preprocessing specific to educational datasets. Support the development of adaptive learning systems and personalized learning experiences through data-driven approaches. Implement and utilize Graph API and OData endpoints to integrate and analyze data from various sources. Work with large language models (LLMs) and retrieval-augmented generation (RAG) techniques to enhance educational tools and provide personalized learning experiences. Leverage vector databases to store and retrieve high-dimensional data efficiently. Requirements Bachelor's degree in Business Administration (Big Data Analytics), Economics, Statistics, or a related field. 3-5 years of experience in data science, with a strong focus on predictive analytics and machine learning. Proficiency in Python, SQL, SAS, and advanced Excel. Experience with BI tools like Power BI and Tableau. Strong understanding of psychometric models and advanced statistical methods. Proven experience in implementing Graph API and working with OData endpoints. Experience with large language models (LLMs), retrieval-augmented generation (RAG), and vector databases. Excellent problem-solving skills and ability to communicate complex technical concepts to non-technical stakeholders. Proven track record of improving educational outcomes through data-driven strategies. Strong background in statistical analysis, including exploratory data analysis and inferential statistics. Preferences: Certification in Power BI or similar BI tools. Experience in educational data analysis and resource allocation optimization. Knowledge of NLP techniques and sentiment analysis is a plus. Strong analytical skills and attention to detail. Ability to work in a collaborative team environment and manage multiple projects simultaneously.